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Integration of Cloud-Fog Based Platform for Load Balancing using Hybrid Genetic Algorithm using Bin Packing Technique
Abstract
The smart girds(SGs) are used to accommodate the growing demand of electric systems and monitor the power consumption with bidirectional communication and power flows. Smart buildings as key partners of the smart grid for the energy transition.Smart grids coordinate the needs and capabilities of all generators, grid operators, end-users and electricity market stakeholders to operate all parts of the system as efficiently as possible, minimising costs and environmental impacts while maximising system reliability, resilience and stability. The users demand for energy varies dynamically in different time slots. The power grids needs ideal load balancing for supply and demand of electricity between end-users and utility providers. The main characteristics of the SGs are its heterogeneous architecture that includes reduce the costly impact of blackouts, help measure and reduce energy consumption ,reduce their carbon footprint and provides the power quality for the range of needs. The cloud-fog based computing model is used to achieve the objective of load balancing in the SG. The cloud layer provides on-demand delivery of resources. The fog layer is the extension of the cloud that lies between the cloud and end-user layer. The fog layer minimizes the latency, enhances the reliability of cloud facilities and reduced the load on the cloud because fog is an edge computing and it analyzing data close to the device that collected the data can make the difference between averting disaster and a cascading system failure. The end-users required electricity through the Macrogrids(MGs) and Utilites installed on fog and cloud layer respectively. The cloud-fog computing framework uses different algorithms for load balancing objective. In this papper, three algorithms are used such as Round Robin (RR), throttled and Hybrid Genetic Algorithm using Bin Packing Technique for load balancing.
... Hybrid approaches use a combination of approximate, accurate, and basic methods to achieve load balancing in fog networks [48][49][50][51][52][53]. This section looks at studies that used hybrid methodologies. ...
... The proposed virtual machine load-balancing technique outperformed the other strategies in the study. To increase the communication between consumers and the electrical supplier, Ali et al. [51] presented a four-layered SG-based architecture, which covered a large area of residents. For VM allocation, three load balancing strategies were used, with the service Electronics 2022, 11, 566 7 of 18 broker policies used for simulations being the most dynamically reconfigurable and closest to data centers. ...
... By employing bin pack approaches, Zubair et al. [51] employed a Genetic Algorithm (GA), throttle and Round Ronin (RR) for load-balancing mechanisms. In this study, an SG was combined with fog, as well as a cloud-based model and three locations with some buildings. ...
The present technological era significantly makes use of Internet-of-Things (IoT) devices for offering and implementing healthcare services. Post COVID-19, the future of the healthcare system is highly reliant upon the inculcation of Artificial-Intelligence (AI) mechanisms in its day-to-day procedures, and this is realized in its implementation using sensor-enabled smart and intelligent IoT devices for providing extensive care to patients relative to the symmetric concept. The offerings of such AI-enabled services include handling the huge amount of data processed and sensed by smart medical sensors without compromising the performance parameters, such as the response time, latency, availability, cost and processing time. This has resulted in a need to balance the load of the smart operational devices to avoid any failure of responsiveness. Thus, in this paper, a fog-based framework is proposed that can balance the load among fog nodes for handling the challenging communication and processing requirements of intelligent real-time applications.
... To accomplish load balancing in fog networks, hybrid methods apply such various methods as approximate, exact, and fundamental [68], [69], [70], [71], [72], [73]. Studies with hybrid methods are reviewed in this section. ...
... Three load balancing mechanisms were applied for allocation of VM, and the service broker policies applied for simulations are dynamically reconfigurable and were the closest to data centers. For resource allocation Zubair, et al. [71] used Genetic Algorithm (GA), throttle, and RR for load balancing mechanism by applying bin pack techniques. In this study, an SG was integrated with fog, and the cloud-based model and three places with some buildings were considered. ...
... COMSATS University, Pakistan [37], [38], [30] [61], [62], [63] [68], [69], [70], [71] Approximate, Fundamental, and Hybrid methods ...
Recently, fog computing has been introduced as a modern distributed paradigm and complement to cloud computing to provide services. Fog system extends storing and computing to the edge of the network, which can solve the problem about service computing of the delay-sensitive applications remarkably besides enabling the location awareness and mobility support. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of Service (QoS) parameters such as resource utilization, throughput, cost, response time, performance, and energy consumption can be improved with load balancing. In recent years, some researches in load balancing techniques in fog networks have been carried out, but there is no systematic review to consolidate these studies. This article reviews the load-balancing mechanisms systematically in fog computing in four classifications, including approximate, exact, fundamental, and hybrid methods (published between 2013 and August 2020). Also, this article investigates load balancing metrics with all advantages and disadvantages related to chosen load balancing mechanisms in fog networks. The evaluation techniques and tools applied for each reviewed study are explored as well. Additionally, the essential open challenges and future trends of these mechanisms are discussed.
... Three LB algorithms were used to allocate VMs, while the service broker policies were dynamically changeable. Bin pack techniques were employed by Zubair et al. [254] as an LB mechanism together with the genetic algorithm (GA), throttle, and RR for resource allocation. In this study, an SG was combined with fog, and three locations at certain buildings and a cloud-based model were assumed. ...
The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.
... Zubair et al. [18] proposed a hybrid technique using genetic algorith m and bin packing technique to minimize the latency issue in smart grids. Bin packing mechanism can optimize the efficient usage of VMs placement whereas genetic algorith m helps to select appropriate VM. ...
Emergence of IoT applications and distributed computing has propelled the development of computing services which can handle dynamic requests at the network edge. Fog computing paradigm has evolved tremendously over the years for achieving above objective. Resource management in fog layer always remains the hot spot which is required to be addressed through some efficient load balancing techniques. Heuristic, Meta-heuristic, Probabilistic, Graph theory based and hybrid load balancing techniques are developed over the past few years to manage workload incurred at the fog servers. This paper provides the brief description of such methods and their comparative analysis in a tabular form. Major area of focus is the overall technique, simulation tool, parameters of evaluation, advantages and disadvantages of the proposed load balancing approaches. Potential researchers can carry forward and extend this research at the next level after analysing the research gaps from the literature survey.
... A multilayer structure was employed to allocate resources in [41]. In this model, load balancing techniques like genetic algorithms, round robin, and throttle were employed to create packing techniques and allocate resources. ...
Fog computing has become a new trend in the Internet of things domain and cloud computing applications. It is a novel model to achieve the availability, flexibility and better responding time. In spite of that, there is so many challenges facing computing environments such as the misuse of the resources and load-balancing between them, which has a major effect on performance. The requirement of effective and robust load-balancing algorithms is one of the most significant interestin this field. Many researchers suggested various load-balancing algorithms in fog computing, but there is still inefficiency in the system performance and misalignment in load -balancing. This paper will provide a description of numerous concepts such as computing fog, fog nodes, load balancing and then werecommenda load-balancing algorithm to enhance the fog-computing environmentperformance, which is a hybrid algorithm benefits from the optimizing processing time (OPT) algorithms. In order to explorethe proposedalgorithmperformance, a comparison made with other algorithms. Results indicates that using the proposed optimizing processing timealgorithmin load-balancing algorithmhas superior response and processing time than the compared algorithms touser requests, and the data total cost centre’s as well.
With the increasing use of the Internet of Things (IoT) in various fields and the need to process and store huge volumes of generated data, Fog computing was introduced to complement Cloud computing services. Fog computing offers basic services at the network for supporting IoT applications with low response time requirements. However, Fogs are distributed, heterogeneous, and their resources are limited, therefore efficient distribution of IoT applications tasks in Fog nodes, in order to meet quality of service (QoS) and quality of experience (QoE) constraints is challenging. In this survey, at first, we have an overview of basic concepts of Fog computing, and then review the application placement problem in Fog computing with focus on Artificial intelligence (AI) techniques. We target three main objectives with considering a characteristics of AI-based methods in Fog application placement problem: (i) categorizing evolutionary algorithms, (ii) categorizing machine learning algorithms, and (iii) categorizing combinatorial algorithms into subcategories includes a combination of machine learning and heuristic, a combination of evolutionary and heuristic, and a combinations of evolutionary and machine learning. Then the security considerations of application placement have been reviewed. Finally, we provide a number of open questions and issues as future works.
The development of novel Information and Communication Technology (ICT) based solutions becomes essential to meet the ever increasing rate of global urbanization in order to satiate the constraint in resources. The popular ‘smart city paradigm is characterized by ubiquitous cyber provisions for the monitoring and control of such city's critical infrastructures, encompassing healthcare, environment, transportation and utilities among others. In order to manage the numerous services keeping their Quality of Service (QoS) demands upright, it is imperative to employ context aware computing as well as fog computing simultaneously. This paper investigates the feasibility of energy minimization at the fog layer through intelligent sleep and wake-up cycles of the fog nodes which are context-aware. It proposes a virtual machine management approach for effectively allocating service requests with a minimal number of active fog nodes using a genetic algorithm (GA); and thereafter, a reinforcement learning (RL) approach is incorporated to optimize the period of fog nodes’ duty cycle. Simulations are carried out using MATLAB and the results demonstrate that the proposed scheme improves energy consumption of the fog layer by approximately 11–21% when compared to existing context sharing based algorithms.
Sensors in Internet of Things generate a huge amount of data. The massive volume of the captured data is stored on cloud servers. Over time, the unbalanced load servers prevent better resource utilization. It also increases the input and the output response time. Hence, applying load balancing techniques is very important to achieve efficient system performance. Ensuring the critical data availability in such dynamic systems is very essential. In this article, the authors propose ROBUST for ensuring data availability mechanism and a fault-tolerance architecture. ROBUST also realises load balancing among servers within minimum latency, by avoiding the problem of the overloaded sites and unbalanced use of the resources on the servers. Comparing the response time using the ROBUST and Load Balancing in the Cloud Using Specialization (LBCS), ROBUST architecture has given satisfactory results in terms of critical data latency. Compared with LBCS, ROBUST gains 42% critical data recovery from a primary server and 45% when searching for duplicated critical data. The authors implemented the system using the JADE platform.
In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE)and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes(TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.
In this paper, a demand side management (DSM) scheme is used to make energy utilization more efficient. The DSM scheme encourages the consumer to change energy utilization patterns which benefit the utility. In return, the consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, reduced peak to average ratio (PAR), and maximization of consumer comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm called hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both former algorithms. A comparison of these algorithms is also presented in terms of three performance parameters. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. A regional based time of use (ToU) tariff is used to make the system effective for different types of regions. Moreover, this enables the consumers to act according to the regional environment. The coordination can play a very important role in cost reduction as well as in consumer comfort maximization. The coordination is incorporated among the electrical devices by using cooperative game theory (GT) and dynamic programming (DP). Extensive simulations are performed to show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization
In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.
Internet of Things (IoT) based Smart grids (SGs) represent a vision of future power systems which helps to provide electricity in a smart and user friendly way. Demand side management is one of the most important component of a SG which allows energy consumers to change their electricity consumption patterns to reduce the electricity consumption cost. In this work, we propose a home energy management system which helps to achieve our desired objectives: reduced electricity consumption cost, peak to average ratio (PAR) and maximize user comfort. For this purpose, we have proposed a scheduling technique which is a hybrid of already existing optimization techniques: bacteria foraging algorithm (BFA) and harmony search algorithm (HSA) and is named as hybrid bacterial harmony (HBH) algorithm. Being producer of electricity units to the consumers, a utility establishes an incentive based pricing tariff; we, on top of it have employed seasonal time of use (ToU) tariff which allows consumers to take decisions regarding their consumption patterns. Moreover, we introduce the concept of coordination among smart appliances using dynamic programming (DP) approach. The coordination among appliances is achieved by the help of the large data generated from the appliances of multiple homes with the joint work of heuristic techniques and DP. The resultant coordination not only reduces the electricity cost but also increases the user comfort. At last, we evaluate the performance of our proposed energy management system using our proposed optimization technique HBH. To comparatively evaluate the performance of our proposed technique, we compare it with already existing techniques. Simulation results validate that the proposed technique effectively accomplish the desired objectives while considering the consumer comfort.
Cloud data centres are faced with the serious problem of increasing energy consumption. Thus, the problem of virtual machine placement for energy saving is becoming a critical issue. Considering various requirements of cloud providers and users, a many-objective virtual machine placement model is built to minimize energy consumption and maximize load balance, resource utilization, and robustness. An EEKnEA (Energy-Efficient KnEA) algorithm is proposed to address this problem. EEKnEA is improved by proposing an Energy-Efficient-oriented Population Initialization Strategy (EEPIS) based on the KnEA (Knee Point-Driven Evolutionary Algorithm), which is a high-performance algorithm for many-objective problems. The proposed model and performance of EEKnEA are evaluated in comparison to KnEA and other algorithms. Experimental results show that the proposed model is reasonable, and the EEKnEA algorithm outperforms its counterparts on this type of problem in terms of energy saving, load balance and robustness.
Traditional electric generation based on fossil fuel consumption threatens the humanity with global warming, climate change, and increased carbon emission. Renewable resources such as wind or solar power are the solution to these problems. The smart grid is the only choice to integrate green power resources into the energy distribution system, control power usage, and balance energy load. Smart grids employ smart meters which are responsible for two-way flows of electricity information to monitor and manage the electricity consumption. In a large smart grid, smart meters produce tremendous amount of data that are hard to process, analyze and store even with cloud computing. Fog computing is an environment that offers a place for collecting, computing and storing smart meter data before transmitting them to the cloud. This environment acts as a bridge in the middle of the smart grid and the cloud. It is geographically distributed and overhauls cloud computing via additional capabilities including reduced latency, increased privacy and locality for smart grids. This study overviews fog computing in smart grids by analyzing its capabilities and issues. It presents the state-of-the-art in area, defines a fog computing based smart grid and, gives a use case scenario for the proposed model.
Database-as-a-Service is offered by many cloud service providers and could significantly reduce the IT infrastructure maintenance bills for enterprises. However, many hesitate migrating their data storage and processing to the cloud, and one of the reasons are data security issues. Encryption could provide sound security guarantees, but existing approaches often gravitate to one of the extremes: either high security and very limited functionality, or vice versa. This paper proposes a flexible database encryption scheme allowing multiple users to perform complex search-based queries, including cross-table
joins, capable of utilizing database’s indexing capabilities. Several proxy servers in between the user and the database server help to achieve this and make it all transparent for the user. The proposed solution demonstrates a practical performance level in most of our experiments.
This paper gives a comprehensive discussion on applying the cloud computing technology as the new information infrastructure for the next-generation power system. First, this paper analyzes the main requirements of the future power grid on the information infrastructure and the limitations of the current information infrastructure. Based on this, a layered cloud-based information infrastructure model for next-generation power grid is proposed. Thus, this paper discussed how different categories of the power applications can benefit from the cloud-based information infrastructure. For the demonstration purpose, this paper develops three specific cloud-enabled power applications. The first two applications demonstrate how to develop practical compute-intensive and data-intensive power applications by utilizing different layered services provided by the state-of-the-art public cloud computing platforms. In the third application, we propose a cloud-based collaborative direct load control framework in a smart grid and show the merits of the cloud-based information infrastructure on it. Some cybersecurity considerations and the challenges and limitations of the cloud-based information infrastructure are also discussed.
The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.
Smart grid (SG), regarded as the next-generation electric grid, will use advanced power, communication, and information technologies to create an automated, intelligent, and widely distributed energy delivery network. In this article, we explore how cloud computing (CC), a next-generation computing paradigm, can be used for information management of the SG and present a novel SG information management paradigm, called Cloud Service-Based SG Information Management (CSSGIM). We analyze the benefits and opportunities from the perspectives of both the SG and CC domains. We further propose a model for CSSGIM and present four motivating applications.
Cloud services are widely used in manufacturing, logistics, digital applications, and document processing. Cloud services must be able to handle tens of thousands of concurrent requests and to enable servers to seamlessly provide the amount of load balancing capacity required to respond to incoming application traffic in addition to allowing users to obtain information quickly and accurately. In the past, researchers have proposed the use of static load balancing or server response times to evaluate load balancing capacity, a lack of which may cause a server to load unevenly. In this study, a dynamic annexed balance method is used to solve this problem. Cloud load balancing (CLB) takes into consideration both server processing power and computer loading, thus making it less likely that a server will be unable to handle excessive computational requirements. Finally, two algorithms in CLB are also addressed with experiments to prove the proposed approach is innovative.
Energy sustainability and environmental preservation have become worldwide concerns with the many manifestations of climate change and the continually increasing demand for energy. As cities and nations become more technologically advanced, electricity consumption rises to levels that may no longer be manageable if left unattended. The Smart Grid offers an answer to the shift to more sustainable technologies such as distributed generation and microgrids. A general public awareness and adequate attention from potential researchers and policy makers is crucial. This paper presents an overview of the Smart Grid with its general features, functionalities and characteristics. It presents the Smart Grid fundamental and related technologies and have identified the research activities, challenges and issues. It demonstrates how these technologies have shaped the modern electricity grid and continued to evolve and strengthen its role in the better alignment of energy demand and supply. Smart Grid implementation and practices in various locations are also unveiled. Concrete energy policies facilitate Smart Grid initiatives across the nations. Interestingly, Smart Grid practices in different regions barely indicate competition but rather an unbordered community of similar aspirations and shared lessons.
A reliable and efficient communications system is required for the robust, affordable and secure supply of power through Smart Grids (SG). Computational requirements for Smart Grid applications can be met by utilizing the Cloud Computing (CC) model. Flexible resources and services shared in network, parallel processing and omnipresent access are some features of Cloud Computing that are desirable for Smart Grid applications. Eventhough the Cloud Computing model is considered efficient for Smart Grids, it has some constraints such as security and reliability. In this paper, the Smart Grid architecture and its applications are focused on first. The Cloud Computing architecture is explained thoroughly. Then, Cloud Computing for Smart Grid applications are also introduced in terms of efficiency, security and usability. Cloud platforms’ technical and security issues are analyzed. Finally, cloud service based existing Smart Grid projects and open research issues are presented.
In recent years, under the concern of energy crisis, the government has actively cooperated with research institutions in developing smart meters. As the Internet of Things (IoT) and home energy management system become popular topics, electronic appliance recognition technology can help users identifying the electronic appliances being used, and further improving power usage habits. However, according to the power usage habits of home users, it is possible to simultaneously switch on and off electronic appliances. Therefore, this study discusses electronic appliance recognition in a parallel state, i.e. recognition of electronic appliances switched on and off simultaneously. This study also proposes a non-invasive smart meter system that considers the power usage habits of users unfamiliar with electronic appliances, which only requires inserting a smart meter into the electronic loop. Meanwhile, this study solves the problem of large data volume of the current electronic appliance recognition system by building a database mechanism, electronic appliance recognition classification, and waveform recognition. In comparison to other electronic appliance recognition systems, this study uses a low order embedded system chip to provide low power consumption, which have high expandability and convenience. Differing from previous studies, the experiment of this study considers electronic appliance recognition and the power usage habits of general users. The experimental results showed that the total recognition rate of a single electronic appliance can reach 96.14%, thus proving the feasibility of the proposed system.
This paper examines the many competing policy issues surrounding the development of the smart grid, including supporting technological innovation and protecting consumer privacy. The paper uses potential privacy-protection regulation as a lens through which to envision smart grid development, as privacy protections will determine, in many ways, information flows and network architectures. The paper concludes with recommended structures for regulating the disclosure and trafficing of smart grid information about consumer electricity usage. This paper was originally presented to the Colorado Public Utitlies Commission in May 2009, and then was used as a framing document in opening the Colorado PUC's investigatory docket concerning smart grid privacy concerns: Docket No. 09I-593EG.
The emergence of cloud computing has established a trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers and cloud computing also provide opportunities to help the grid with respect to robustness and load balancing. To gain insights into these opportunities, we formulate the service request routing problem in cloud computing jointly with the power flow analysis in smart grid and explain how these problems can be related. Simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that a grid-aware service request routing design in cloud computing can significantly help in load balancing in the electric grid and making the grid more reliable and more robust with respect to link breakage and load demand variations.
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