Smart Grid Management Using Cloud and Fog Computing
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Cloud computing provides Internet-based services to its consumer. Multiple requests on cloud server simultaneously cause processing latency. Fog computing act as an intermediary layer between Cloud Data Centers (CDC) and end users, to minimize the load and boost the overall performance of CDC. For efficient electricity management in smart cities, Smart Grids (SGs) are used to fulfill the electricity demand. In this paper, a proposed system designed to minimize energy wastage and distribute the surplus energy among energy deficient SGs. A three-layered cloud and fog based architecture described for efficient and fast communication between SG’s and electricity consumers. To manage the SG’s requests, fog computing introduced to reduce the processing time and response time of CDC. For efficient scheduling of SG’s requests, proposed system compare three different load balancing algorithms: Round Robin (RR), Active Monitoring Virtual Machine (AMVM) and Throttled for SGs electricity requests scheduling on fog servers. Dynamic service broker policy is used to decide that which request should be routed on fog server. For evaluation of the proposed system, results performed in cloud analyst, which shows that AMVM and Throttled outperform RR by varying virtual machine placement cost at fog servers.
KeywordsSG Management Load Balancing Algorithm Service Broker Policy Election Petitions Cloud Server
Software as a Service (SaaS)
Platform as a Service (PaaS)
Infrastructure as a Service (IaaS)
In cloud computing, scalability is a big challenge because connected devices and cloud response time are directly proportional according to . In delay, sensitive applications system need fast communication. The concept of fog computing is recommended to minimize the intensity of such challenges. The fog computing provides same services as cloud computing and acts as an intermediary between cloud servers and consumer to reduce the overall load of cloud servers. The concept of fog computing helps to minimize latency, enhance the flexibility and reliability of the network. In this proposed system, fog computing services such as networking, storage and computation used in SG for efficient energy management of smart cities. Fog computing provides delay sensitive communication between consumer and servers. Information Communication and Technology (ICT) convert Traditional Grid (TG) into SG for two-way communication. SG connected with other SGs and electricity consumers to fulfill the energy requirements. SGs share information with fog servers, fog servers maintain the data of SGs and predict the future load on SGs. Fog servers have information about surplus and deficient SGs of a region. Fog helps to arrange energy for SGs in case of deficiency via cloud and utility. If a whole region becomes energy deficient than fog communicates with cloud and cloud assign utility company to that region to fulfill the energy demand. In this paper, the proposed system covers a large residential and commercial area of six regions. Every region has its own fog servers to manage the requests of SGs. Every building and Power Supply Stations (PSS) has its own SG to fulfill the electricity demand. Every SG communicate with other SGs to make a coordination system for electricity sharing. In case of energy deficiency SG requests to its corresponding fog to fulfill the electricity demand. Fog manages the request and coordinates with other SGs, and if fog found surplus energy in any other SG then assign that surplus energy to deficient energy SG. If there is no surplus energy in all SGs of a region then fog requests to cloud, and cloud server assigns appropriate utility company to deficient SG. To balance the load on fog proposed system use three load balancing algorithms RR, Throttled and AMVM load balancing algorithm (Fig. 2).
Fog computing provides delay sensitive communication between consumer and servers. The demand for electricity is increasing gradually. To minimize the electricity wastage and satisfy the user requirements in the efficient and effective manner we need to schedule its resources and consumption. Random electricity demand by the consumers causes the problem to fulfill the energy requirements. Authors in [3, 4, 5] explore the idea of energy scheduling by integrating TG with ICT for two-way communication. For fast, flexible and reliable communication among different consumers, the system requires cloud computing. Authors in [3, 5] proposed the idea of cloud computing with the intermediary layer of fog computing to schedule energy demand. Cloud and fog computing help in runtime decision making . The authors in , specified that the system needs to predict the demand for energy to minimize the wastage. In [6, 7] they proposed load predictor systems using SG. SG use to communicate with other SGs and developed a coordination network between different electricity consumer to guess overall load. Fog computing uses to minimize the load and processing time of cloud. Fog act as an intermediary layer between consumer and cloud servers consist of VMs to perform in-network processing. To make the fog efficient and fast system need load balancing algorithms and service broker policies to distribute the load among different VMs of fog. Authors in [4, 5] designed cloud environment for limited users of different regions to check the performance of their proposed load balancing algorithms. Authors emphasis on the optimization of the overall response time of fog servers for residential buildings by overlooking the overall system costs. Proposed system motivated to optimize overall response time and communication cost of fog computing during scheduling of electricity requests received from SGs.
In this paper, the proposed system minimizes the response and processing time of fog computing and reduces the overall system cost.
2 Related Work
For fast and delay sensitive communication, cloud load management is a challenging field for researchers. To minimize the processing time of cloud, authors are working on different mechanisms. They introduce an intermediary layer of fog computing. Fog computing helps to achieve fast response time for the consumer as compared to the cloud. Fog provides the runtime decision-making capabilities with the help of its computing devices, that is used to distribute the overall load of fog. Many algorithms are proposed to balance the load on cloud and fog computing and help to reduce the VMs cost and fog processing time. Most commonly used algorithms are Round Robin (RR), throttled, honeybee foraging and biased random sampling etc. These algorithms are used to schedule the user requests on fog with minimum latency. The authors in , described a cloud load balancing algorithm and compare its results with RR, Throttled and honeybee foraging. Their proposed system performs better and balances the load successfully in a multiuser environment.
The researchers in , designed a honeybee foraging inspired system that minimizes the overall system processing and waiting time of a request in the queue to assign VM. Their proposed system performs better than adoptive, adaptive-dynamic and heat diffusion based dynamic load balancing algorithms. The authors in , proposed Ant Colony optimization (ACO) to overcome the problem of resource allocation in fog. And compare ACO’s energy consumption, processing time and standard deviation with RR. They claim that their ACO consume minimum energy with minimum processing time as compare to RR.
Another system that allocates the task efficiently to balance the overall load discussed in . Proposed model allocates low power tasks and improves the system throughput by reducing system delay. In , authors compare Cloud-Based Demand Response (CBDR) with Distributed Demand Response (DDR) and show that CBDR performs better than DDR with respect to reliability and scalability of the communication network. DDR is channel dependent and within few iterations returns optimal solution. DDR is unreliable due to information loss. While CBDR is channel independent and cost-efficient. The authors in , compare six load balancing algorithms that are RR, throttled, Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO) and sixth is a hybrid of ABC and ACO algorithm called Hydride Artificial Bee and Ant Colony Optimization (HABACO). Their proposed HABACO outperform the other five discussed algorithms.
3 Problem Formulation
4 Proposed System
The proposed system is designed to fulfill electricity demand of consumers using cloud and fog computing efficiently in term of cost and processing time. This section, describes the architecture of our proposed system, where we use the three-layered architecture that consists of cloud, fog and consumer layers. These three layers are interconnected to share information with each other. The fog computing act as an intermediary layer between cloud servers and consumers. Proposed system deals with two types of electricity demand, electric vehicles energy demands consider as commercial consumption (Power supply stations) and household demand as residential consumption (Buildings, homes and apartments). Every building and power supply station has its own SG to generate and fulfil the energy demand of its connected users. SGs communicate with fog server to share their information about energy generation and demand. For an energy deficient SG, fog server will predict the energy demand and provide energy, from energy surplus (energy generation greater than energy demand) SG. If all the SGs become energy deficient then fog server sends their information to cloud server for utility assignment. Utility company is the electricity provider at large scale.
4.1 RR Load Balancing Algorithm
RR load balancing algorithm maintains a table of the request with respect to time of arrival. It has a scheduler that maintains request with respect to time. RR algorithm allocating multiple requests coming from the users and assign VMs one by one (Fig. 3).
4.2 AMVM Load Balancing Algorithm
AMVM load balancer works just like throttled. Users requests are queued by the data center controller as they are received. First, the availability of the VM is checked and if the VM is available the request is removed from the beginning of the queue and allocated to VM. The status of the VM is changed from available to busy. After the execution of the request, the status of the changed back from busy to available.
4.3 Throttled Load Balancing Algorithm
5 Simulations and Results
In this paper, simulations of the proposed system performed in cloud analyst tool. Proposed system schedule SGs requests at fog using load balancing algorithms that are RR, Throttled and AMVM. These algorithms are used for load balancing at fog servers. Results simulated on the base of electricity requests generated by the consumers in 24 h. Simulation results performed using six regions with one fog server and two fog servers in each region shown in Fig. 4.
5.1 Twelve Fog Servers
5.2 Six Fog Servers
In this paper proposed system represents a model of SG management using cloud and fog infrastructure. The comparison results of three load balancing algorithms perform. Results show that for efficient processing in fog computing AMVM and throttled algorithm perform batter with limited fog resources. Throttled and AMVM algorithms support system scalability with minimum response and processing time. Throttled and AMVM performs better than RR in both scenarios by varying number of fog servers. Various load balancing algorithms and broker policies are compared to improve and reduce the response time and delay respectively, of the fog servers. The response time and VM placement cost of fog servers are inversely proportional and there is a tradeoff between fog placement cost and processing time. The future work includes such intelligent load balancing algorithms that warn fog ahead of time about the predicted user requests for the resources from different regions of the world.
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