Question: MOMCC: A market-oriented framework for mobile cloud computing grounded in service-oriented architecture.

This comprehensive survey explores the critical issue of energy consumption on the smartphone platform, a pivotal concern given the increasing demands placed on mobile devices. The unrestricted mobility of these systems is largely contingent upon the power supply from the device's battery, which often falls short in capacity, particularly as smartphones are continually enhanced with new features. This mismatch is exacerbated by the disparity between Moore's law, which guarantees a doubling of processing power every two years, and the stagnation in battery technology, which has barely doubled in the last ten years. This discrepancy has prompted a paradigm shift in the research and development of mobile networks, protocols, and devices, necessitating a focus not just on wireless data rates, but more critically on energy efficiency.

The primary challenge in the design of mobile systems is no longer solely the speed of the wireless connection, but rather the energy limitations that arise as consumers demand more power-intensive services. These include faster internet connectivity, multiple air interfaces, and extended standby or operational times for their devices, all of which place a significant strain on battery life. To address this, the survey meticulously evaluates the energy consumption of various components within a mobile device, such as the wireless interfaces, display, MP3 player, and other elements, providing comparative analysis.

The measurement outcomes elucidate which components are the most energy-intensive, offering valuable insights for the development of future mobile protocols and applications. These findings enable designers to optimize energy usage and enhance the overall efficiency of mobile devices. The comprehensive data and additional results are made accessible to the public on the researchers' webpage, providing a resource for the ongoing advancement of mobile technology.

Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.

Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.

Modern automotive industry uses embedded systems and corresponding Electronic Control Units (ECU) for a wide variety of purposes from entertainment to vehicle control. The use of these embedded systems became crucial to an extent that the performance of these embedded systems can severely affect the performance of the automobile. So the industry tries to develop new and efficient systems periodically, to improve the performance of the whole system. As the number of ECUs in an automobile increased, new technologies were developed to interconnect these ECUs. As a result of much efficient ECUs, the data produced by these ECUs and the data to be transmitted between these ECUs increased to a very large scale. The industry tried to select new connectivity solutions which provide higher bandwidth. The automotive embedded system industry thus ended up in using the well accepted and highly standardized Ethernet connectivity. This paved the way to develop new technologies for automotive embedded system. Service Oriented Architecture (SOA) has proved to be an efficient and flexible software design paradigm for high level systems. The main factors behind its success were the amount of scalability and reusability provided by this platform. The usage of SOA over the automotive Ethernet can provide the benefit of SOA to the automotive embedded world. Such an approach needs a deep feasibility study to identify whether such integration is possible, and to discover architectures and protocols which support it.

There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are "green field" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.

Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.

While cloud computing has brought paradigm shifts to computing services, researchers and developers have also found some problems inherent to its nature such as bandwidth bottleneck, communication overhead, and location blindness. The concept of fog/edge computing is therefore coined to extend the services from the core in cloud data centers to the edge of the network. In recent years, many systems are proposed to better serve ubiquitous smart devices closer to the user. This article provides a complete and up-to-date review of edge-oriented computing systems by encapsulating relevant proposals on their architecture features, management approaches, and design objectives.

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.

Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units - a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.


Question: MOMCC: A market-oriented design for mobile cloud computing grounded in service-oriented architecture.

This paper presents a series of strategies to fortify online reputation reporting systems against the detrimental impacts of unfair ratings and discriminatory practices. In the digital marketplace, reputation systems serve as crucial tools for mitigating risks, yet they can be undermined when buyers collude to provide misleading evaluations to sellers or when sellers offer unequal service quality to buyers based on undisclosed criteria. To address these vulnerabilities, we introduce and assess a suite of mechanisms designed to neutralize or substantially diminish the adverse effects of such manipulative actions. These mechanisms are engineered for seamless integration into current online reputation systems, thereby enhancing their resilience against potentially deceitful behaviors from both buyers and sellers, ensuring the systems' reliability and fairness.

Web service technology can be used for integrating heterogeneous and autonomous applications into cross-organizational systems. A key problem is to support a high quality of service-oriented systems despite vulnerabilities caused by the use of external web services. One important aspect that has received little attention so far is risk management for such systems. This paper discusses risks peculiar for servicebased systems, their impact and ways of mitigation. In the context of service-oriented design, risks can be reduced by selection of appropriate business partners, web service discovery, service composition and Quality of Service (QoS) management. Advisors. Vincenzo D’Andrea.

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-perform ant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.

Web services based on the service-oriented architecture framework provide a suitable technical foundation for making business processes accessible within enterprises and across enterprises. But to appropriately support dynamic business processes and their management, more is needed, namely, the ability to prescribe how Web services are used to implement activities within a business process, how business processes are represented as Web services, and also which business partners perform what parts of the actual business process. In this paper, the relationship between Web services and the management of business processes is worked out and presented in a tutorial-like manner.

Resource scheduling based on SLA(Service Level Agreement) in cloud computing is NP-hard problem. There is no efficient method to solve it. This paper proposes an optimal model for the problem and give an algorithm by applying stochastic integer programming technique. Applying Gröbner bases theory for solving the stochastic integer programming problem and the experimental results of the implementation are also presented.

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.

Modern enterprises almost ubiquitously deploy middlebox processing services to improve security and performance in their networks. Despite this, we find that today's middlebox infrastructure is expensive, complex to manage, and creates new failure modes for the networks that use them. Given the promise of cloud computing to decrease costs, ease management, and provide elasticity and fault-tolerance, we argue that middlebox processing can benefit from outsourcing the cloud. Arriving at a feasible implementation, however, is challenging due to the need to achieve functional equivalence with traditional middlebox deployments without sacrificing performance or increasing network complexity.
 In this paper, we motivate, design, and implement APLOMB, a practical service for outsourcing enterprise middlebox processing to the cloud.
 Our discussion of APLOMB is data-driven, guided by a survey of 57 enterprise networks, the first large-scale academic study of middlebox deployment. We show that APLOMB solves real problems faced by network administrators, can outsource over 90% of middlebox hardware in a typical large enterprise network, and, in a case study of a real enterprise, imposes an average latency penalty of 1.1ms and median bandwidth inflation of 3.8%.

In the past few years, wireless sensor networks (WSNs) have been gaining increasing attention because of their potential of enabling of novel and attractive solutions in areas such as industrial automation, environmental monitoring, transportation business, health-care etc. If we add this collection of sensor derived data to various Web-based social networks or virtual communities, blogs etc., we can have a remarkable transformation in our ability to "see" ourselves and our planet. Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications. However, the computational tools needed to launch this exploration may be more appropriately built from the data center "Cloud" computing model than the traditional HPC approaches. In this paper, we propose a framework to enable this exploration by integrating sensor networks to the emerging data center "cloud" model of computing. But there are many challenges to enable this framework. We propose a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications. Also there is a need for internetworking cloud providers in case of violation of service level agreement with users. We discussed these issues and proposed reasonable solutions.

This article introduces the Follow-Me Cloud concept and proposes its framework. The proposed framework is aimed at smooth migration of all or only a required portion of an ongoing IP service between a data center and user equipment of a 3GPP mobile network to another optimal DC with no service disruption. The service migration and continuity is supported by replacing IP addressing with service identification. Indeed, an FMC service/application is identified, upon establishment, by a session/service ID, dynamically changing along with the service being delivered over the session; it consists of a unique identifier of UE within the 3GPP mobile network, an identifier of the cloud service, and dynamically changing characteristics of the cloud service. Service migration in FMC is triggered by change in the IP address of the UE due to a change of data anchor gateway in the mobile network, in turn due to UE mobility and/or for load balancing. An optimal DC is then selected based on the features of the new data anchor gateway. Smooth service migration and continuity are supported thanks to logic installed at UE and DCs that maps features of IP flows to the session/service ID.

Mobile computing is emerging as the prime focus of next generation computing .One of the prime issues of mobile computing is to provide infrastructure support in terms of computing devices, seamless mobility, application middleware, data and user security, and user applications/services. Mobile commerce is one of the driving forces that has evinced enormous interest in mobile computing .The thought of conducting commerce on the go is what is driving the huge investments corporations are making in researching this area. This paper discusses the various challenges in providing infrastructure for wireless computing.

Nowadays, IoT services are typically delivered as physically isolated vertical solutions, in which all system components ranging from sensory devices to applications are customized and tightly coupled for the requirements of each specific project. The efficiency and scalability of such service delivery model are intrinsically limited, posing significant challenges to IoT solution providers. Therefore, we propose a novel PaaS framework that provides essential platform services for IoT solution providers to efficiently deliver and continuously extend their services. This paper first introduces the IoT PaaS architecture, on which IoT solutions can be delivered as virtual verticals by leveraging computing resources and middleware services on cloud. Then we present the detailed mechanism and implementation of domain mediation, which helps solution providers to efficiently provide domain-specific control applications. The proposed approaches are demonstrated through the implementation of a domain mediator for building management and two use cases using the mediator.


Question: MOMCC: A market-oriented framework for mobile cloud computing based on service-oriented architecture.

This paper explores the synergy between web services and business process management within the context of service-oriented architecture (SOA). Web services, as a key component of SOA, offer a robust technical framework for enabling business processes to be accessible and interoperable both within and across organizations. However, to fully support the dynamic nature of business processes and facilitate their effective management, additional capabilities are required. These include the ability to define how web services are utilized to execute specific activities within a business process, how business processes themselves can be embodied as web services, and the determination of which business partners are responsible for executing various components of the actual process. The paper delves into the intricate relationship between web services and business process management, presenting a comprehensive tutorial that elucidates how these two domains can be integrated to enhance the agility and efficiency of business operations.

With the maturity of service-oriented architecture (SOA) and Web technologies, web services have become critical components of Software as a Service (SaaS) applications in cloud ecosystem environments. Most SaaS applications leverage multi-tenant data stores as a back end to keep and process data with high agility. Although these technologies promise impressive benefits, they put SaaS applications at risk against novel as well as prevalent attack vectors. This security risk is further magnified by the loss of control and lack of security enforcement over sensitive data manipulated by SaaS applications. An effective solution is needed to fulfill several requirements originating in the dynamic and complex nature of such applications. Inspired by the rise of Security as a Service (SecaaS) model, this paper introduces "Information Flow Control as a Service (IFCaaS)". IFCaaS lays the foundation of cloud-delivered IFC-based security analysis and monitoring services. As an example of the adoption of the IFCaaS, this paper presents a novel framework that addresses the detection of information flow vulnerabilities in SaaS applications. Our initial experiments show that the framework is a viable solution to protect against data integrity and confidentiality violations leading to information leakage.

Network Service Providers should offer provisioning services with guaranteed Quality of Service (QoS), specifically adapted to the characteristics of the applications running on their network. In this paper we propose the Network Control Layer (NCL), a software framework solution based on Software Defined Networks (SDN), OpenFlow and Network as a Service (NaaS) paradigms. It addresses a major innovation area in the field of network control and management providing on-demand end-to-end network provisioning services with guaranteed QoS, based on the specific requirements of on-top running interactive applications. The NCL implementation is based on the OpenNaaS framework, and it includes mechanisms for network status monitoring and SDN switches configuration based on the interactive applications' QoS network requirements. We demonstrate NCL's utility in the context of control plane models making use of a practical use case.

Trust management is one of the most challenging issues in the emerging cloud computing area. Over the past few years, many studies have proposed different techniques to address trust management issues. However, despite these past efforts, several trust management issues such as identification, privacy, personalization, integration, security, and scalability have been mostly neglected and need to be addressed before cloud computing can be fully embraced. In this article, we present an overview of the cloud service models and we survey the main techniques and research prototypes that efficiently support trust management of services in cloud environments. We present a generic analytical framework that assesses existing trust management research prototypes in cloud computing and relevant areas using a set of assessment criteria. Open research issues for trust management in cloud environments are also discussed.

Existing cellular networks suffer from inflexible and expen sive equipment, complex control-plane protocols, and vend orspecific configuration interfaces. In this paper, we argue th at software defined networking (SDN) can simplify the design and management of cellular data networks, while enabling new services. However, supporting many subscribers, frequent mobility, fine-grained measurement and control, and real-time adaptation introduces new scalability challeng es that future SDN architectures should address. As a first step , we present a software-defined cellular network architectur that (i) allows controller applications to express policie s based on the attributes of subscribers, rather than network addre sses and locations, (ii) enables real-time, fine-grained contro l via a local agent on each switch, and (iii) extends switches to support features like deep packet inspection and header com pression to meet the needs of cellular data services, (iv) su pports flexible “slicing” of network resources based on the at tributes of subscribers, rather than the packet header field s, and flexible “slicing” of base stations and radio resources b y having the controller to handle radio resource management, admission control and mobility in each slice.

Migration of legacy systems to more advanced technologies and platforms is a current issue for many software organizations. Model-Driven Modernization combined with Software as a Service delivery model is a very promising approach, which possesses a lot of advant ges, including reduced costs, automation of migration ac tivities and reuse of system functionality. However, a drawback of such an innovative modernization approach is that it lacks mature software process models to guide its adoption. Thus , a methodology for seamless execution of different mig ration and deployment activities is quite needed. On the other hand, agile development methods have been successfully adopted in various projects, which partly or thoroughly use the engineering and delivery models exploited in the modernization process. This paper presents how a pa rticular methodology for Model-Driven Modernization with deployment to the Cloud is enriched with agile tech niques to address different challenging issues. The extended agile methodology could be used by organizations which ha ve already applied agile software development as well as by organizations that plan to introduce it in their work. KeywordsCloud computing; Agile Methodology; Modeldriven Modernization; Software as a service

Now customers can opt for software and information technology services according to his requirements and can get these services on a leased basis from the network service provider and this has the facility to scale its requirements to up or down. This service is known as cloud computing, provided by the infrastructure provider which is a third party provider. Cloud computing provides many advantages to the customer like scalability, better economics of scaling, its ability to recover from problems, its ability to outsource non-core activities and flexibility. Cloud computing is a better option for the organizations to take as their best option without any initial investment and day by day frequent and heavy use of cloud computing is increasing but despite all the benefits a cloud offers to an organization there are certain doubts and threats regarding the security issues associated with a cloud computing platform. The security issues primarily involve the external control over organizational structure and management and personal and private data of the organization can be compromised. Personal and private data in this computing environment has a very high risk of breach of confidentiality, integrity and availability. Growth of cloud computing is mainly hampered due to these security concerns and challenges. Proper security arrangements are need to be placed before selecting the service provider for any cloud computing service and customers need to be very careful about understanding the risks of security breaches and challenges of using this new computing environment. This detailed study discusses about some of the challenges associated with cloud computing services and security issues related to this platform.

Public Clouds today provide a variety of services for data analysis such as Amazon Elastic MapReduce and Google BigQuery. Each service comes with a pricing model and service level agreement (SLA). Today's pricing models and SLAs are described at the level of compute resources (instance-hours or gigabytes processed). They are also different from one service to the next. Both conditions make it difficult for users to select a service, pick a configuration, and predict the actual analysis cost. To address this challenge, we propose a new abstraction, called a Personalized Service Level Agreement, where users are presented with what they can do with their data in terms of query capabilities, guaranteed query performance and fixed hourly prices.

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.

Wireless networks such as mobile networks, with their inflexible and expensive network infrastructure, are facing various challenges in efficiently handling the exponentially growing traffic demands of users. Hence, mobile network operators (MNOs) are looking forward to novel networking paradigms which could simplify the task of network management and control and allow faster deployment of newer solutions on top of existing hardware by software updates. Software defined networking (SDN) is a revolutionary technology which makes networks more agile and flexible by separation of data plane and control plane tasks. SDN is playing a key role in deploying mobile network services on network function virtualization (NFV) infrastructure for elastic and flexible deployment of core network services e.g., virtualization of evolved packet core in LTE to efficiently handle huge control signal overhead and traffic demands from machine-to-machine and internet of things devices. Besides, NFV and SDN are offering scalable, flexible and adaptable network service chaining platforms as a replacement for inflexible middleboxes. Mobile edge computing (MEC) is a new evolving platform which brings together IT services and cloud computing for offering end-users network aware services and solutions. In this survey, we enumerate numerous issues and challenges in designing SDN based wireless networks and review various SDN based seminal solutions for 4G/5G. Finally, this survey presents the role of SDN, NFV and MEC in designing 5G networks.

Cloud federations have been formed to share the services, prompt and support cooperation, as well as interoperability among their already deployed cloud systems. However, the creation and management of the cloud federations lead to various security issues such as confidentially, integrity and availability of the data. Despite the access control policies in place, an attacker may compromise the communication channel processing the access requests and the decisions between the access control systems and the members(users) and vice-versa. In cloud federation, the rating of the services offered by different cloud members becomes integral to providing the users with the best quality services. Hence, we propose an innovative blockchainbased framework that on the one hand permits secure communication between the members of the federation and the access control systems, while on the other hand provides the quality services to the members by considering the service constraints imposed by them.


Question: A marketplace for exchanging electrical power within smart grid communities.

In the evolving landscape of the electricity sector, the SmartGrid represents a transformative shift, poised to revolutionize the traditional grid that has remained largely unchanged for a century. The SmartGrid's potential for value creation lies in its interconnected nature, fostering an ecosystem that heavily leverages Information Technology (IT) at multiple levels for enhanced monitoring and control capabilities. This paradigm shift is underpinned by the convergence of the Internet of Things (IoT) and the Internet of Services, with cooperation serving as the cornerstone for enabling these interactions. The SmartGrid is characterized by a complex network of diverse, collaborating entities that work in tandem to deliver its envisaged functionalities. The future of the SmartGrid will see the emergence of sophisticated business services that capitalize on the rapid exchange of information among all stakeholders in near real-time. Achieving this vision necessitates a significant reliance on Cyber-Physical Systems (CPS), which will play a pivotal role in overseeing, sharing, and managing information and actions across both the business and physical realms. As an intrinsic component of the SmartGrid, CPS will be tasked with addressing a myriad of open challenges to ensure the seamless operation of this interconnected system.

Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers' energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home's power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RAkEL), and a bespoke multi-label classification algorithm (MLkNN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an ac-distributed system is proposed. This paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed, taking into account the special nature of a low-voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop-control method, the proposed controller uses resistive output impedance, and as a result, a different control law is obtained. The controller is implemented by using a digital signal processor board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6-kVA inverters connected in parallel, showing the features of the proposed wireless control

High-penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g., by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e., local). In general, we find that local control schemes are able to maintain voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.

Efficient energy management is becoming an important issue when designing any electrical system. Recently, a significant effort has been devoted to the design of optimized micro and nanogrids comprising residential area subsystems. One of these approaches consists on the design of a dc-based nanogrid optimized for the interoperation of electric loads, sources, and storage elements. Home appliances are one of the main loads in such dc-based nanogrids. In this paper, the design and optimization of an appliance for operation in a dc-based nanogrid is detailed. An induction heating cooktop appliance will be considered as a reference example, being some of the design considerations generalizable to other appliances. The main design aspects, including the inductor system, power converter, EMC filter, and control are considered. Finally, some simulation results of the expected converter performance are shown.

Recent development in Renewable Energy Sources (RES) have led to a higher penetration in existing power systems. As the majority of RES are intermittent by nature, it presents major challenges to the grid operators. An Energy Storage System (ESS) can be connected to mitigate this intermittent sources. When multiple renewable energy sources, flexible loads and ESS are connected to the grid, complexity of the system is significantly increased. Such problems have multiple constraints and objective hence it is challenging to design an effective rule-based control strategy. A control strategy based on fuzzy logic which is similar to human reasoning tolerates uncertainties and imprecision. The proposed fuzzy inference system (FIS) aims to reduce the grid fluctuation and increase the energy storage life-cycle by deciding when and how much to charge/discharge the ESS. A real data was used to test and validate the proposed FIS. In this paper, MATLAB/Simulink is used to create and implement the microgrid test bench and FIS. The proposed methodology is tested against a rule-based control strategy. Simulation studies were carried out on the developed model and results have shown that the proposed FIS can effectively reduce the fluctuation and prolong the life cycle of the ESS.

-Constant power loads (CPLs) may yield instability due to the well-known negative impedance characteristic. This study analyzes the factors which cause instability of a DC microgrid with multiple DC-DC converters. Two stabilization methods are presented for two operation modes: constant voltage source mode and droop mode. And sufficient conditions for the stability of the DC microgrid are obtained by identifying the eigenvalues of the Jacobian matrix. The key is to transform the eigenvalue problem to a quadratic eigenvalue problem (QEP). When applying the methods in practical engineering, the salient feature is that the stability parameter domains can be estimated by the available constraints, such as the values of capacities, inductances, maximum load power and distances of the cables. Compared with some classical methods, the proposed methods have wider stability region. The simulation results based on MATLAB /simulink platform verify the feasibility of the methods.

M-Bus Systems are used to support the remote data exchange with meter units through a network. They provide an easy extend able and cost effective method to connect many meter devices to one coherent network. Master applications operate as concentrated data collectors and are used to process measured values further. As modern approaches facilitate the possibility of a Smart Grid, M-Bus can be seen as the foundation of this technology. With the current focus on a more effective power grid, Smart Meters and Smart Grids are an important research topic. This bachelor thesis first gives an overview of the M-Bus standard and then presents a Java library and API to access M-Bus devices remotely in a standardized way through Web Services. Integration into common IT applications requires interoperable interfaces which also facilitate automated machine-to-machine communication. The oBIX (Open Building Information Exchange) standard provides such standardized objects and thus is used to create a Web Service gateway for the Java API.

This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system

As electric vehicles (EVs) take a greater share in the personal automobile market, their penetration will cause overload conditions at the distribution transformer. This paper focuses on the impacts of charging EVs on residential distribution networks including the transformer. The cost to accommodate a large-scale EV penetration by upgrading distribution transformers can be prohibitive. To alleviate the potential new load peaks with minimal infrastructure investments, a demand response strategy is proposed as a load shaping tool that allows improvement in distribution transformer usage. With the proposed strategy, consumers' preferences, load priorities, and privacy can be taken into account.


Question: A marketplace for exchanging electrical power within smart grid communities.

Embracing the Smart Grid Revolution: Opportunities and Challenges
In recent times, the transformation of traditional power grids into smart grids (SGs) has emerged as a pivotal development in the global energy landscape. The SG, characterized by its advanced capabilities and interconnectedness, is viewed as a critical pathway to achieving a sustainable and resilient energy future. As climate change concerns intensify, there is a pressing need to integrate smart energy solutions that can optimize resource utilization and reduce environmental impact. This article delves into the core principles of SGs, highlighting their technological intricacies and the significant challenges they present. Moreover, it discusses the potential avenues for the IEEE Industrial Electronics Society (IES) to contribute to this evolving frontier, emphasizing the role of innovation and collaboration in shaping the future of energy delivery. The transition to SGs not only promises enhanced efficiency and reliability in electricity distribution but also opens up new opportunities for market dynamics, including the trading of electricity in smart grid neighborhoods. As SGs become more prevalent, the role of advanced analytics, automation, and consumer engagement in energy markets becomes increasingly crucial.

Negotiation processes taking place between two or more parties need to agree on a viable execution mechanism. Auctioning protocols have proven useful as electronic negotiation mechanisms in the past. Auctions are a way of determining the price of a resource in a dynamic way. Additionally, auctions have well defined rules such as winner and loser determination, time restrictions or minimum price increment. These restrictions are necessary to ensure fair and transparent resource allocation. However, these rules are limiting flexibility of consumers and providers. In this paper we introduce a novel negotiation based resource allocation mechanisms using the offer-counteroffer negotiation protocol paradigm. This allocation mechanism shows similarities to the supermarket approach as consumer and provider are able to communicate directly. Further, the price is determined in a dynamic way similar to auctioning. We developed a Bazaar-Extension for CloudSim which simulates negotiation processes with different strategies. In this paper we introduce and analyze a specific Genetic Algorithm based negotiation strategy. For the comparison of the efficiency of resource allocations a novel Bazaar-Score was used.

High-penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g., by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e., local). In general, we find that local control schemes are able to maintain voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.

Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.

In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques. Appl. Sci. 2015, 5 1135

With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address-in a unifying framework-service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.

The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.

Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (1018 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates.
 We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different power-response characteristics. We show that with a power budget of 4.75 MW, we can obtain up to 5.2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1.7X improvement.

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system

Constant power loads (CPLs) are often the cause of instability and no equilibrium of dc microgrids. In this study, we analyze the existence and stability of equilibrium of dc microgirds with CPLs and the sufficient conditions for them are provided. To derive the existence of system equilibrium, we transform the problem of quadratic equation solvability into the existence of a fixed point for an increasing fractional mapping. Then, the sufficient condition based on the Tarski fixed-point theorem is derived. It is less conservative compared with the existing results. Moreover, we adopt the small-signal model to predict the system qualitative behavior around equilibrium. The analytic conditions of robust stability are determined by analyzing quadratic eigenvalue. Overall, the obtained conditions provide the references for building reliable dc microgrids. The simulation results verify the correctness of the proposed conditions.


Question: A marketplace for exchanging electrical power within smart grid communities.

A Vision for an Information-Centric Energy System: The Berkeley Perspective
This document outlines a novel strategy for crafting a power grid infrastructure that is more adaptable, robust, and scalable, designed to promote efficient energy consumption, incorporate localized power generation, and balance demand through continuous monitoring of electricity supply and usage patterns. Drawing parallels from the transformative impact of the Internet on communication networks, where intelligence is decentralized to the periphery and the complexity of underlying technologies is abstracted through standardized interfaces, we envision a similar paradigm for the energy sector. In this model, all end-user devices function as either energy consumers or producers, with smart endpoints dynamically adjusting their energy demands in accordance with the grid's capacity, thus ensuring optimal utilization and sustainability of the energy ecosystem. This approach not only enhances the resilience of the power grid but also facilitates the seamless integration of renewable energy sources, fostering a more sustainable and efficient energy market for smart grid communities.

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

Active management of distribution networks and different controllable resources will play a key role in future Smart Grids. This paper proposes that centralized active network management functionalities at MV level could also include an algorithm which in real time confirms reliable islanding detection with local measurements based passive method. The proposed algorithm continuously ensures that there is such reactive power unbalance, so that the operation point remains constantly outside the non-detection zone of the used passive islanding detection method. The effect of recent grid code requirements on the proposed scheme, like the P/f -droop control of DG units, has been also considered.

-Constant power loads (CPLs) may yield instability due to the well-known negative impedance characteristic. This study analyzes the factors which cause instability of a DC microgrid with multiple DC-DC converters. Two stabilization methods are presented for two operation modes: constant voltage source mode and droop mode. And sufficient conditions for the stability of the DC microgrid are obtained by identifying the eigenvalues of the Jacobian matrix. The key is to transform the eigenvalue problem to a quadratic eigenvalue problem (QEP). When applying the methods in practical engineering, the salient feature is that the stability parameter domains can be estimated by the available constraints, such as the values of capacities, inductances, maximum load power and distances of the cables. Compared with some classical methods, the proposed methods have wider stability region. The simulation results based on MATLAB /simulink platform verify the feasibility of the methods.

Increasing penetration level of Distributed Generators (DGs)/ Renewable energy sources (RES) creates low inertia problems, damping effect on grid dynamic performance and stability. Voltage rise by reverse power from PV generations, excessive supply of electricity in the grid due to full generation of the DGs, power fluctuations due to variable nature of Renewable energy sources, and degradation of frequency regulation can be considered as some negative results of mentioned hurdle. For securing such a grid there should add inertia, virtually. The proposed control approach based on an inverter connected in Microgrid, which operated as a virtual synchronous generator (VSG) reacting only in transitory situations. Super capacitor (SC), an energy storage system (ESS) included in MG-forming inverter i.e., a normal inverter structure used for contributing system robustness and reliability. In steady-state load distributed between the MG-supporting inverter are achieved with two restoration controllers for active and reactive powers. The virtual inertia hypothesis can be used for maintaining a large share of DGs in future grids without affect system stability. This proposed system analyzed and simulated using MATLAB/SIMULINK.

This paper introduces a robust learning scheme that can dynamically predict the stability of the reconnection of subnetworks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deploymen t of self sufficient networks, or microgrids, becomes more likel y. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islandi ng and reconnecting said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynam ics simulator fed with pre-acquired system parameters is used t o create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating poin ts to ensure diversity. Accuracies of approximately 90% were obs erved throughout most conditions when making dynamic predictions of a given network. Keywords—Synchrophasor, machine learning, microgrid, islanding, reconnection I. I NTRODUCTION As we make strides towards a smarter power system, it is important to explore new techniques and innovations to fully capture the potential of such a dynamic entity. Many large blackout events, such as the blackout of 2003, could have been prevented with smarter controls and better monito ring [1]. Phasor measurement units, or PMUs, are one such breakthrough that will allow progress to be made in both monitoring and implementing control to the system [2]. PMUs allow for direct measurement of bus voltages and angles at high sample rates which makes dynamic state estimation more feasible [3], [4]. With the use of PMUs, it is possible to improve upon current state estimation [5] and potentially o pen up new ways to control the grid. The addition of control techniques and dynamic monitoring will be important as we begin to integrate newer solutions, such as microgrids, int o the power network. With these advanced monitoring devices, microgrids become more feasible due to the potential for rea ltime monitoring schemes. The integration of microgrids bri ng many benefits such as the ability to operate while islanded as well as interconnected with the main grid; they provide a smooth integration for renewable energy sources that matc h C. Lassetter, E. Cotilla-Sanchez, and J. Kim are with the Sch ool of Electrical Engineering & Computer Science, Oregon State University, C orvallis, OR, 97331 USA e-mail:{lassettc,ecs,kimjinsu }@oregonstate.edu local demand. Unfortunately the implementation of microgr ids is still challenging due to lacking experience with the beha vior of control schemes during off-nominal operation. Currently, microgrids are being phased in slowly due in part to the difficulty of operating subnetworks independent ly as well as determining when they can be reconnected to the main grid. Works in the literature have focused on the potential of reconnecting microgrids to the main grid, in particular aiming at synchronizing the buses at points of interconnect with respects to their voltages, frequencies , and angles [6]. Effort has been directed at creating control sch emes to minimize power flow at the point of common coupling (PCC) using direct machine control, load shedding, as well a s energy storage, to aid in smooth reconnection [7], [8]. Upon reconnection of an islanded sub-network to the main grid, instability can cause damage on both ends. It is important to track instabilities on both the microgrid and main grid upon reconnection to accurately depict the outcome of reconnect ion. Most works focus on the synchronization of two networks before reconnection [9], [10]. In some cases we may need to look at larger microgrids or subnetworks in which multipl e PCCs exist. In such scenarios, it becomes much more difficult to implement a control scheme that satisfies goodreconnection tolerances in regards to minimizing bus frequency, angle, a nd voltage differences at each PCC. In addition to the possibil ity of multiple PCCs, it is possible that direct manipulation of the system becomes limited, compromised, or unsupported with respect to synchronization. In order to address these short comings, we implement an algorithm that dynamically tracks and makes predictions based on the system states, providing real-time stability information of potential reconnectio ns. An algorithm that tracks potential reconnection and island ing times needs to be robust to prevent incorrect operation during these scenarios. PMUs make use of GPS synchronization [11] which can create an attack platform for adversarie s by changing or shifting the time synchronization. Incorrec t or compromised usage could lead to incorrect predictions that would degrade system stability due to hidden failures that remain dormant until triggered by contingencies [12]. We propose an algorithm that can make accurate predictions in face of potentially compromised measurements. Due to the complexity of the power grid, it is difficult to come up with a verbatim standard depicting the potential sta bility after reconnection of a subnetwork. With advances in the artificial intelligence community, we can make use of machin e learning algorithms in order to explore vast combinations o f sensor inputs, states, and control actions. This can be done in a

Because business-to-business (B2B) electronic marketplaces (e-marketplaces) facilitate transactions between buyers and sellers, they strive to foster a trustworthy trading environment with a variety of trust-building measures. However, little research has been undertaken to explore trust-building measures used in B2B e-marketplaces, or to determine to what extent these measures are applied in B2B e-marketplaces and how they are applied. Based on reviews of the scholarly, trade, and professional literature on trust in electronic commerce, we identified 11 trustbuilding measures used to create trust in B2B e-marketplaces. Zucker’s trust production theory [1986] was applied to understand how these trust-building measures will enhance participants’ trust in buyers and sellers in B2B e-marketplaces or in B2B e-marketplace providers. A descriptive content analysis of 100 B2B e-marketplaces was conducted to survey the current usage of the 11 trust-building measures. Many of the trust-building measures were found to be widely used in the B2B e-marketplaces. However, although they were proven to be effective in building trust-related beliefs in online business environments, several institutional-based trustbuilding measures, such as escrow services, insurance and third-party assurance seals, are not widely used in B2B e-marketplaces.

Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.
 In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.

P-f and Q-V droop methods are the most common decentralized control methods in islanded microgrid. Although with the P-f droop method, an accurate active power sharing can be achieved among distributed energy resources (DERs), by Q-V droop, the reactive power sharing among DERs often deteriorates due to its highly dependence on the power line impedances and the local load. Variation of frequency and voltage by load changes is another challenge in droop control method. In this paper, a new autonomous control method is proposed to share reactive power among DERs accurately and restore frequency of a microgrid. The proposed method does not require any communication link and so maintains reliability and simplicity of the network. The synchronizing among DERs is obtained by the load change detection, which is accomplished by wavelet transform. The method operation principle is explained and analyzed. Simulation results are presented to validate the effectiveness of the proposed method.

We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with PLANNER, CONNIVER, HEARSAY-n, and PUP6 are used to demonstrate that negotiation--the two-way transfer of information--is a natural extension to the transfer of control mechanisms used in earlier problem-

Global electrical grids are verging on the largest technological transformation since the introduction of electricity into the home. The antiquated infrastructure that delivers power to our homes and businesses is being replaced with a collection of digital systems called the smart grid. This grid is the modernization of the existing electrical system that enhances customers' and utilities' ability to monitor, control, and predict energy use.


Question: A marketplace for exchanging electrical power within smart grid communities.

This review highlights strategies for enhancing the efficiency of heterogeneous server clusters through power and energy management, taking into account service-level information like request priorities set by service-level agreements. By optimizing resource allocation and leveraging insights from service agreements, server systems can achieve greater energy conservation and operational efficiency without compromising service quality.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A novel algorithm for solving the economic dispatch problem: Krill Herd Algorithm
The economic dispatch (ED) problem is a significant issue in electrical power systems operation. This paper introduces a novel algorithm, the Krill Herd Algorithm (KHA), to solve the ED problem, particularly when it involves valve-point effects. KHA is a population-based metaheuristic algorithm that imitates the foraging behavior of krill in marine ecosystems. The proposed method is tested on several standard test systems and compared with other well-known optimization algorithms. Results show that KHA can effectively solve the ED problem with valve-point effects, providing competitive solutions.

**Similar Documents**: This study presents an innovative approach, the Krill Herd Algorithm (KHA), for addressing the economic dispatch (ED) problem in power systems, especially when dealing with valve-point effects. The ED problem is a critical concern in the operational planning of electrical power systems. KHA, inspired by the feeding behavior of krill in the ocean, is a population-based optimization algorithm. Its effectiveness in solving the ED problem with valve-point effects is demonstrated through comparative analysis with established optimization techniques on benchmark test systems, showcasing its ability to deliver high-quality solutions.

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.

Information systems can serve as intermediaries between the buyers and the sellers in a market, creating an “electronic marketplace” that lowers the buyers' cost to acquire information about seller prices and product offerings. As a result, electronic marketplaces reduce the inefficiencies caused by buyer search costs, in the process reducing the ability of sellers to extract monopolistic profits while increasing the ability of markets to optimally allocate productive resources. This article models the role of buyer search costs in markets with differentiated product offerings. The impact of reducing these search costs is analyzed in the context of an electronic marketplace, and the allocational efficiencies such a reduction can bring to a differentiated market are formalized. The resulting implications for the incentives of buyers, sellers and independent intermediaries to invest in electronic marketplaces are explored. Finally, the possibility to separate price information from product attribute information is introduced, and the implications of designing markets promoting competition along each of these dimensions are discussed. Copyright © 1997 by The Institute of Management Sciences

Energy accounting determines how much a software principal contributes to the total system energy consumption. It is the foundation for evaluating software and for operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track all hardware usage by software in a heterogeneous multicore system like modern smartphones and tablets.
 In this work, we argue that energy accounting should be formulated as a cooperative game and that the Shapley value provides the ultimate ground truth for energy accounting policies. We reveal the important flaws of existing energy accounting policies based on the Shapley value theory and provide Shapley value-based energy accounting, a practical approximation of the Shapley value, for battery-powered mobile systems. We evaluate this approximation against existing energy accounting policies in two ways: (i) how well they identify the top energy consuming applications, and (ii) how effective they are in system energy management. Using a prototype based on Texas Instruments Pandaboard and smartphone workload, we experimentally demonstrate existing energy accounting policies can deviate by 400% in attributing energy consumption to running applications and can be up to 25% less effective in system energy management when compared to Shapley value-based energy accounting.

Remote electrification with island supply systems, the increasing acceptance of the microgrids concept and the penetration of the interconnected grid with DER and RES require the application of inverters and the development of new control algorithms. One promising approach is the implementation of conventional f/U-droops into the respective inverters, thus down scaling the conventional grid control concept to the low voltage grid. Despite contradicting line parameters, the applicability of this proceeding is outlined and the boundary conditions are derived

High-penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g., by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e., local). In general, we find that local control schemes are able to maintain voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.

Machine-to-machine (M2M) communications have emerged as a cutting edge technology for next-generation communications, and are undergoing rapid development and inspiring numerous applications. This article presents an investigation of the application of M2M communications in the smart grid. First, an overview of M2M communications is given. The enabling technologies and open research issues of M2M communications are also discussed. Then we address the network design issue of M2M communications for a home energy management system (HEMS) in the smart grid. The network architecture for HEMS to collect status and power consumption demand from home appliances is introduced. Then the optimal HEMS traffic concentration is presented and formulated as the optimal cluster formation. A dynamic programming algorithm is applied to obtain the optimal solution. The numerical results show that the proposed optimal traffic concentration can minimize the cost of HEMS.

Negotiation processes taking place between two or more parties need to agree on a viable execution mechanism. Auctioning protocols have proven useful as electronic negotiation mechanisms in the past. Auctions are a way of determining the price of a resource in a dynamic way. Additionally, auctions have well defined rules such as winner and loser determination, time restrictions or minimum price increment. These restrictions are necessary to ensure fair and transparent resource allocation. However, these rules are limiting flexibility of consumers and providers. In this paper we introduce a novel negotiation based resource allocation mechanisms using the offer-counteroffer negotiation protocol paradigm. This allocation mechanism shows similarities to the supermarket approach as consumer and provider are able to communicate directly. Further, the price is determined in a dynamic way similar to auctioning. We developed a Bazaar-Extension for CloudSim which simulates negotiation processes with different strategies. In this paper we introduce and analyze a specific Genetic Algorithm based negotiation strategy. For the comparison of the efficiency of resource allocations a novel Bazaar-Score was used.

Global electrical grids are verging on the largest technological transformation since the introduction of electricity into the home. The antiquated infrastructure that delivers power to our homes and businesses is being replaced with a collection of digital systems called the smart grid. This grid is the modernization of the existing electrical system that enhances customers' and utilities' ability to monitor, control, and predict energy use.

A topology for bidirectional non-isolated multiport DC-DC converter is proposed in this paper. The energy generation from the renewable sources such as wind and solar aie intermittent in nature. A battery port is mandatory for this purpose. Hence, a high gain multiport bidirectional converter for DC micro-grid application with high conversion efficiency, reduced size, and low cost is proposed. The bidirectional power flow capability through a feedback back path to charge a battery from the load capacitor is also provided. Therefore, the battery is not only charged from the input source but also from the load capacitor. The number of outputs are independent from each other and the battery is charged from the second output. Steady state operation and theoretical analysis of the converter proposed is elaborated. To validate the performance and theoretical concepts, it is simulated in MATLAB/Simulink software. The results of simulation verify the appropriate performance of proposed topology.