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Efficient Resource Provisioning for Smart Buildings Utilizing Fog and Cloud Based Environment
Abstract
The integration of Smart Grid (SG) with cloud computing promises to develop an improved energy management system for utilities and consumers. New applications and services are developed which create large amount of data to be processed on cloud. Fog computing as an extension of cloud computing which helps to mitigate load on cloud data centers. In this paper, a three layered model based on cloud and fog framework is proposed to reduce load of consumers and power generation system. End user layer contains clusters of buildings which are connected to fog server layer. Fog layer is an intermediate layer which connects the end user layer to cloud layer. Three load balancing algorithms Round Robin (RR), throttled and proposed Particle Swarm Optimization with Simulated Annealing (PSOSA) are used for resource allocation. The service broker policy considered in this paper is optimized response time. The findings demonstrate that PSOSA performs better than RR and throttled in order to alleviate response time, processing time and cost of virtual machine, microgrid and data transfer.
... A new advance service broker policy is proposed for optimal fog selection.Using this policy the results are compared between RR and TA. In [2]different algorithms are used to task management in the demand side. Load balancing techniques are used fo distributing the task among of different Virtual Machines (VMs) on fog and cloud. ...
... Load balancing techniques are used fo distributing the task among of different Virtual Machines (VMs) on fog and cloud. also in [2] authors used fog and cloud environment with SG to overcome the limitations. By inspired [1] and [2], in this paper, we introduced another load balancing algorithm HRRN for efficient resource allocation and load balancing. ...
... also in [2] authors used fog and cloud environment with SG to overcome the limitations. By inspired [1] and [2], in this paper, we introduced another load balancing algorithm HRRN for efficient resource allocation and load balancing. The main focus of this paper to reduce the RT. ...
Cloud computing is an embedded computing technology that uses the internet to support large applications and servers to provide services to end users belongs to different companies. It has made accessibility of resources in flexible manners and improves the output and performance. However, in cloud computing load balancing and hosting of data centers on the internet creates unpredictable network latency. Resolved this problem by new computing model called Fog computing. In this paper, we are using cloud and fog computing environment in Smart Grid (SG) in an effective manner. Cloud and fog computing environment hold the energy management of groups of smart buildings. When a number of users increase cloud and fog load also increases and Response Time (RT) is too much high. So it is a big problem to reduce the RT. In this paper Highest Response Ratio Next (HRRN) algorithm, Round Robin (RR) and Throttled Algorithm (TA) are considered to reduce the RT. While considering these algorithms results are compared to each other.
... A new advance service broker policy is proposed for optimal fog selection.Using this policy the results are compared between RR and TA. In [2]different algorithms are used to task management in the demand side. Load balancing techniques are used fo distributing the task among of different Virtual Machines (VMs) on fog and cloud. ...
... Load balancing techniques are used fo distributing the task among of different Virtual Machines (VMs) on fog and cloud. also in [2] authors used fog and cloud environment with SG to overcome the limitations. By inspired [1] and [2], in this paper, we introduced another load balancing algorithm HRRN for efficient resource allocation and load balancing. ...
... also in [2] authors used fog and cloud environment with SG to overcome the limitations. By inspired [1] and [2], in this paper, we introduced another load balancing algorithm HRRN for efficient resource allocation and load balancing. The main focus of this paper to reduce the RT. ...
Cloud is a pool of virtualized resources. Integrating cloud in a smart grid environment helps to efficiently utilize the energy resources while fulfilling the energy demands of residential users. However, when number of users increase it is difficult to efficiently utilize the cloud resources to handle so many user requests. Fog reduce the latency, processing and response time of user requests. In this paper, cloud-fog based environment for efficient energy management is proposed. The objective of achieving maximum performance is also formulated mathematically in this paper. Simulations in CloudAnalyst are performed to compare and analyze the performance of load balancing algorithms: Round Robin (RR), Throttled, and Weighted Round Robin (WRR) and service broker policies: Service Proximity Policy, Optimize Response Time, Dynamically Reconfigure with Load, and New Dynamic Service Proximity. Simulation results showed that Throttled load balancing algorithm give better response time than RR and WRR.
... A new advance service broker policy is proposed for optimal fog selection.Using this policy the results are compared between RR and TA. In [2]different algorithms are used to task management in the demand side. Load balancing techniques are used fo distributing the task among of different Virtual Machines (VMs) on fog and cloud. ...
... Load balancing techniques are used fo distributing the task among of different Virtual Machines (VMs) on fog and cloud. also in [2] authors used fog and cloud environment with SG to overcome the limitations. By inspired [1] and [2], in this paper, we introduced another load balancing algorithm HRRN for efficient resource allocation and load balancing. ...
... also in [2] authors used fog and cloud environment with SG to overcome the limitations. By inspired [1] and [2], in this paper, we introduced another load balancing algorithm HRRN for efficient resource allocation and load balancing. The main focus of this paper to reduce the RT. ...
Cloud computing is an embedded computing technology that uses the internet to support large applications and servers to provide services to end users belongs to different companies. It has made accessibility of resources in flexible manners and improves the output and performance. However, in cloud computing load balancing and hosting of data centers on the internet creates unpredictable network latency. Resolved this problem by new computing model called Fog computing. In this paper, we are using cloud and fog computing environment in Smart Grid (SG) in an effective manner. Cloud and fog computing environment hold the energy management of groups of smart buildings. When a number of users increase cloud and fog load also increases and Response Time (RT) is too much high. So it is a big problem to reduce the RT. In this paper Highest Response Ratio Next (HRRN) algorithm, Round Robin (RR) and Throttled Algorithm (TA) are considered to reduce the RT. While considering these algorithms results are compared to each other.
... Their requests service times are modeled using Integer Linear Programming method. In [21], A. Yasmeen et al. have proposed a Cloud-Fog model that has end user, fog and cloud layers. The end user layer contains devices connected to the fog layer located at the cloud edge. ...
... This section presents the benchmark comparison and analysis of the proposed Load Balancing Scheduling Approach (LBSSA) approach. Some of the standard and most recent scheduling approaches such as Genetic-based Approach (GA) [20], Simulated Annealing (SA) based approach [21] and Dynamic Resource Allocation Method (DRAM) [9] are used for comparison purposes with the proposed LBSSA. ...
Fog computing broadens the computing services to serve requests of Internet of Things (IoT) by resources at the edge of Cloud-Fog environments instead of serving these requests by resources at the environment’s core. The aim of fog computing is to reduce load of computing in data centers and reduce latency of requests, especially real-time ones. Load balancing and scheduling play essential roles and represent main key challenges to guarantee high throughput and reliability of services in Cloud-Fog environments. Therefore, this paper introduces a reliable scheduling approach for allocating customers’ requests to the resources of Cloud-Fog environments. The approach is called Load Balanced Service Scheduling Approach (LBSSA) and it considers load balancing among resources when assigning requests to them by classifying requests to real-time, important and time-tolerant. In addition, scheduling of requests in the proposed approach considers the failure rate of resources in order to provide high reliability for requested services. The approach has a set of algorithms for handling different types of requests. Simulation experiments using CloudSim are conducted to assess the LBSSA approach in terms of number of computing resources, utilization of resources, load balance variance and running time.
... Simulation results showed that the proposed algorithm efficiently allocates resources while minimizing the response time and maximizing the throughput. Furthermore, in [22], a particle swarm optimization algorithm has been introduced for the resource provisioning in Fog-cloud infrastructures specifically focused on Smart buildings. Results showed that their approach can reduce the response time, the data transfer and the cost of VM allocation. ...
... Two constraints are also added to make sure that the formulation only selects one slice and one gateway for each sensor. These two constraints are shown in (22). ...
Recently, with the advent of the Internet of Things (IoT), Smart Cities have emerged as a potential business opportunity for most cloud service providers. However, centralized cloud architectures cannot sustain the requirements imposed by many IoT services. High mobility coverage and low latency constraints are among the strictest requirements, making centralized solutions impractical. In response, theoretical foundations of Fog Computing have been introduced to set up a distributed cloud infrastructure by placing computational resources close to end-users. However, the acceptance of its foundational concepts is still in its early stages. A key challenge still to answer is Service Function Chaining (SFC) in Fog Computing, in which services are connected in a specific order forming a service chain to fully leverage on network softwarization. Also, Low Power Wide Area Networks (LPWANs) have been getting significant attention. Opposed to traditional wireless technologies, LPWANs are focused on low bandwidth communications over long ranges. Despite their tremendous potential, many challenges still arise concerning the deployment and management of these technologies, making their wide adoption difficult for most service providers. In this article, a Mixed Integer Linear Programming (MILP) formulation for the IoT service allocation problem is proposed, which takes SFC concepts, different LPWAN technologies and multiple optimization objectives into account. To the best of our knowledge, our work goes beyond the current state-of-the-art by providing a complete end-to-end (E2E) resource provisioning in Fog-cloud environments while considering cloud and wireless network requirements. Evaluations have been performed to evaluate in detail the proposed MILP formulation for Smart City use cases. Results show clear trade-offs between the different provisioning strategies. Our work can serve as a benchmark for resource provisioning research in Fog-cloud environments since the model approach is generic and can be applied to a wide range of IoT use cases.
... In order to have effective management on resources, load balancing mechanism in a smart home is offered. Yasmeen, et al. [77] presented a three-layered model based on the cloud and fog framework to alleviate the load of consumers and the power generation system. This model was comprised of the core layer (cloud), the intermediate layer (fog) and the end-user layer. ...
... These metrics are measured in most of the resource-management-based papers; hence, we bring them together here side by side. Yasmeen, et al. [77] Fatima, et al. [78] Javaid, et al. [79] Fatima, et al. [80] Abbas, et al. [81] Rehman, et al. [82] Fatima, et al. [83] Gill, et al. [84] ...
Smart homes are equipped residences for clients aiming at supplying suitable services via intelligent technologies. Through smart homes, household appliances as the Internet of Things (IoT) devices can easily be handled and monitored from a far distance by remote controls. With the day-to-day popularity of smart homes, it is anticipated that the number of connections rises faster. With this remarkable rise in connections, some issues such as substantial data volumes, security weaknesses, and response time disorders are predicted. In order to solve these obstacles and suggest an auspicious solution, fog computing as an eminently distributed architecture has been proposed to administer the massive, security-crucial, and delay-sensitive data, which are produced by communications of the IoT devices in smart homes. Indeed, fog computing bridges space between various IoT appliances and cloud-side servers and brings the supply side (cloud layer) to the demand side (user device layer). By utilizing fog computing architecture in smart homes, the issues of traditional architectures can be solved. This paper proposes a Systematic Literature Review (SLR) method for fog-based smart homes (published between 2014 and May 2019). A practical taxonomy based on the contents of the present research studies is represented as resource-management-based and service-management-based approaches. This paper also demonstrates an abreast comparison of the aforementioned solutions and assesses them under the same evaluation factors. Applied tools, evaluation types, algorithm types, and the pros and cons of each reviewed paper are observed as well. Furthermore, future directions and open challenges are discussed.
... Using cloud and fog with SG a network between the end users and the cloud system is made. User communicates with the fog and it proceeds these requests to the cloud [4]. In proposed case user makes requests for electricity to the fog and fog then response back with the required services. ...
... Although proposed model outperformed other techniques on the bases of RT, and cost of VMs. In [4], authors proposed a new algorithm PSO with Simulated Annealing (PSOSA) for load balancing. The idea is to balance the load of users request on cloud and reduce power generation. ...
The integration of Smart Grid (SG) with cloud and fog computing has improved the energy management system. The conversion of traditional grid system to SG with cloud environment results in enormous amount of data at the data centers. Rapid increase in the automated environment has increased the demand of cloud computing. Cloud computing provides services at the low cost and with better efficiency. Although problems still exists in cloud computing such as Response Time (RT), Processing Time (PT) and resource management. More users are being attracted towards cloud computing which is resulting in more energy consumption. Fog computing is emerged as an extension of cloud computing and have added more services to the cloud computing like security , latency and load traffic minimization. In this paper a Cuckoo Optimization Algorithm (COA) based load balancing technique is proposed for better management of resources. The COA is used to assign suitable tasks to Virtual Machines (VMs). The algorithm detects under and over utilized VMs and switch off the under-utilized VMs. This process turn down many VMs which puts a big impact on energy consumption. The simulation is done in Cloud Sim environment, it shows that proposed technique has better response time at low cost than other existing load balancing algorithms like Round Robin (RR) and Throttled.
... They concluded that their research helps to find the linkages between drivers and systems and identify which drivers have the most significant potential to influence a given system while keeping the primary beneficiary in mind. Yasmeen et al. [31] suggested a three-layered model based on cloud and fog frameworks to lower consumer and power generation system load. Three load balancing algorithms were used for resource allocation: Round Robin (RR), throttled and hypothesized Particle Swarm Optimization with Simulated Annealing (PSOSA). ...
More energy is consumed by domestic appliances all over the world. By reducing energy consumption, sustainability can be improved in domestic contexts. Several earlier approaches to this problem have provided a conceptual overview of green and smart buildings. This paper aims to provide a better solution for reducing energy consumption by identifying the fields of abnormal energy consumption. It creates a better environment-friendly smart building to adopt the various lifestyles of people. This paper’s main objective is to monitor and control the energy efficiency of smart buildings by integrating IoT sensors. This paper mainly analyzes various prime factors that can help to improve energy efficiency in smart buildings. Factors impacting energy consumption are analyzed, and outliers of energy consumption are predicted and optimized to save energy. Various parameters are derived from IoT devices to improve energy efficiency in lighting and HVAC controls, energy monitoring, building envelope and automation systems, and renewable energy. The parameters used in water, network convergence, and electrical and environmental monitoring are also used for improving energy efficiency. This paper uses various IoT devices for monitoring and generating data in and around a smart building and analyzes it by implementing an intelligent Information Communication Technology (ICT) model called the Dynamic Semantic Behavior Data Analysis (DSBDA) Model to analyze data concerning dynamic changes in the environment and user behavior to improve energy efficiency and provide better sustainable lifestyle-based smart buildings. From the analyzed output, the outliers of the power consumption and other abnormalities are identified and controlled manually or automatically to improve sustainability regarding energy use in smart buildings.
... Through the utilization of cloud and fog nodes, it is possible to create an SG network link between cloud environments and SG users. The user expresses their requests to the fog nodes, which are relayed to the cloud [13]. In the scenario, the user approaches the fog nodes with a request for electricity, and the fog nodes react by providing the requested services. ...
Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.
... Anila Yasmeen et al. 80 presented a three-layer model for efficient resource provision based on fog and cloud environments. This three-layer model minimizes the power generating system and consumer loads. ...
Resource provisioning is allocating resources to clients over the Internet and plays a prominent role in cloud computing infrastructure‐as‐a‐service (IaaS). The main issues of resource provisioning are meeting user demands like bandwidth, response time, throughput, availability, and so on. The cloud computing framework uses virtualization technology to provide on‐demand access to computer system resources, specifically computation and storage. In cloud computing, computing resources are shared across another communication network using virtualization. However, the cloud computing mechanism cannot meet the needs of a large number of Internet of Things (IoT) services. To consider this, fog computing was introduced as a modified mechanism of the cloud paradigm by providing cloud services for the end devices. Fog computing is a decentralized computing model placed between devices, edges, data centers or the cloud. In this way, it helps to meet the demands initiated by IoT services, such as reduced latency, wide mobility coverage, and so on. Several solutions regarding resource allocation in fog computing are available in the research field, but such solutions have not achieved satisfactory results. Therefore, finding a solution by analyzing recent problems is open research. Therefore, this paper reviews different methods established for resource provisioning in fog computing using different parameters from 2015 to 2021 and introduces the limitations, advantages and future directions related to different resource provisioning techniques.
... Yasmeen et al. [25] proposed a provisioning algorithm for fogging. This method considered security of resource communication and aimed to reduce the response time. ...
The rapid development of internet of things (IoT) gadgets and the increase in the rate of sending requests from these devices to cloud data centers resulted in congestion and consequently service provisioning delays in the cloud data centers. Accordingly, fog computing emerged as a new computing model to address this challenge. In fogging, services are provisioned at the edge of the network using devices with computing and storage capabilities, which are located through the way to connect IoT devices to cloud data centers. Fog computing aims to alleviate the computing load in data centers and cut the delay of requests down, notably real-time and delay-sensitive requests. To achieve these goals, vitally important challenges such as scheduling requests, balancing the load, and reducing energy consumption, which affects performance and reliability in the edge-fog-cloud computing architecture, should be considered into account. In this paper, a reinforcement learning fog scheduling algorithm is proposed to address these challenges. The experimental results indicate that the proposed algorithm raises the load balance and diminishes the response time compared to the existing scheduling algorithms. Additionally, the proposed algorithm outperforms other approaches in terms of the number of used devices.
... The benefits of combining IoT with fog computing are demonstrated through an architectural model and a series of use cases. The authors [49] to lessen the strain on consumers and electricity-producing systems, a three-layered approach cloud is enabled and fog architecture is proposed. The fog server layer is connected to the end-user layer by clusters of buildings. ...
Recently researchers and companies have shown significant interest in merging blockchain and the Internet of Things (IoT) to create a safe, reliable, and resilient communication platform. However, determining the proper role of blockchain in existing IoT contexts with minimum implications is a challenge. This work suggests a message schedule for a blockchain-based architecture with two access-level setting filters for incoming messages: critical and non-critical. The proposed work of the researchers divides the fog layer into two parts: action clusters and blockchain fog clusters. Similar to the three-layered IoT architecture, the action cluster and the main cloud data center work together for critical message requests. The blockchain fog cluster is dedicated to only the blockchain application’s requirements. In the fog layer, a fog broker is used to schedule critical and non-critical messages in the action and blockchain fog clusters, respectively. The proposed technique is compared to the existing Dual Fog-IoT architecture. The solution is also tested for fog and cloud computing resource utilization. The findings demonstrate that this architecture is feasible for varying percentages of receiving critical and non-critical messages. In addition to the inherent benefits of blockchain, the suggested paradigm reduces the system loss rate and offloads the cloud data center with minimal changes to the existing IoT ecosystem.
... A java programming-based NS-3 simulator is used to implement the proposed framework. Similarly, the authors proposed an inter-operable cloud -fog integrated framework for the health care IoT application [18]. Proposed framework divided into four layers and supports the mobility of the patient's data as well as enhanced the diversity of medical cases. ...
Applications of the Internet of Things (IoT) are used in several areas to create a smart environment such as healthcare, smart agriculture, smart cities, transportation, and water management, etc. Due to the high pace of IoT technology adoption, Big Data generation is increasing excessively, requiring an efficient platform like cloud computing to process a large amount of data. On the other hand, time/delay-sensitive and real-time applications cannot be processed in the cloud due to high latency and energy consumption. Hence, a new emerging computing model named fog has emerged to address the mentioned issues and provide a complementary solution. However, Fog nodes provide limited cloud services in minimum delay and energy at the local node, but they cannot process the highly computation-oriented IoT applications. Furthermore, an adaptive cloud-fog integrated framework is proposed to process entire IoT applications and significantly improve the latency, computation cost, load balancing, and energy consumption by accommodating the resources in the form of virtual machine instances. This article exploited the features of two metaheuristic-based techniques Cuckoo Search Optimization (CSO) and Partial Swarm Optimization (PSO). We have developed a secure framework to solve the allocation of the IoT services in the cloud-fog environment while minimizing the mentioned influential parameters. The performance of the proposed framework is rigorously evaluated at synthetic datasets and heterogeneity of resources in fog as well as cloud simulation environment. The simulation results proved that the proposed hybrid metaheuristic algorithm outperforms other baseline policies and improves the various influential parameters.
... A. Yasmeen et al [6] RR (Round Robin), thrust, and recommended are three load balancing algorithms. PSOSA (Simulated Particle Swarm Optimization for Resource Assignment) is utilised for resource assignment. ...
The complete energy efficiency of homogeneous fog networks is examined in this study, including loads, circuit calculation, and electricity use, using a thorough PSO. This investigation. This model allows us to formulate the energy efficiency optimization problem with practical constraints for the computing resources available in helper nodes and unused spectrum for future intelligent Internet of Things (IoT) applications in nearby environments, allowing us to investigate the compromise between performance and energy costs in collaborative loading tasks, allowing us to formulate the energy efficiency optimization problem with practical constraints for the computing resources available in helper nodes and unused spectrum for future intelligent Internet of Things (IoT) applications in nearby environments. Under plausible modulation schemes, our complex technique provides the optimal planning of a task node and numerous nearby help nodes. The compromise between energy economy and performance planning in homogenous fog networks is demonstrated by extensive simulation results.
... Algorithms such as RR, throttled, and PSO-SA are used for load balancing with the function of reducing the response and processing time of these requests, in addition to reducing costs with VMs and data transfer. 44 Other important tasks carried out in this layer are the measurement of users' energy consumption patterns and the initial filtering of data for later sending to the cloud, facilitating basic tasks such as energy sharing between users. For this, Reference 38 indicates that each house must have a smart meter capable to carry out these tasks. ...
The global energy matrix has been transforming in recent years and today it is moving toward a decentralized form of management for the generation of clean energy worldwide. With this, new ways of management for the energy environment has been emerging, being the Energy Cloud (EC), through cloud computing, one of these trends that seek to optimize the process of generation, distribution, storage and energy consumption, making the most flexible and dynamic energy market. Given these motivations, the objective of this article is to discuss the proposal of a series of technical, economic, and environmental standards for energy management in a cloud computing environment. For this, a systematic review was carried out to identify these regulatory suggestions, where through the reading and analysis of 121 articles, 72 suggestions were extracted and classified according to the layers and management support blocks of from EC management environment. Besides, these policies were grouped according to their approach into technical, economic, and environmental factors to identify what was already proposed by the academy's authors and what are the regulatory deficiencies for EC. These regulatory suggestions can be used by policy-makers, researchers, and managers of this innovative energy management environment, which is the EC.
Novelty Statement This study consists of a regulatory proposal for Energy Cloud (EC), an innovative energy management environment. As there is still no regulatory body responsible for the regulation of EC, the main contribution of this article is the proposal of a regulation layer. In this sense, 72 suggestions for standards were raised, presenting a new layer for EC.
... Authors presented a mechanism using FC to make SGs system more responsive by reducing the RT in grid. 26 In this article, FNs act as an edge of the network that speed up the transmission and reduces the RT of the different requests sent from FNs to cloud. Overload and underload concept is driven in the architecture to maintain connection between the devices and produce electricity on the basis of usage. ...
The Internet of Things generates a massive amount of data through sensors and other physical devices, which cause latency and delay in processing time and response time of smart grid (SG) services. To increase the efficiency of SGs, cloud computing provides a pay‐per model approach to transmit the collected data and enhances the scalability and functionality of end devices. Moreover, in load balancing (LB), resource utilization, and distribution mechanism, milliseconds also make an effect where delays or jitters are not acceptable. Fog computing, an extension of cloud provides computing, networking, storage, communication at the edge of the network, and has overcome the existing challenges of SGs. In this article, a new hybrid model on the highly virtualized platform is proposed. Three algorithms for LB: throttled, round robin, and particle swarm optimization, are analyzed and compared. Moreover, this article also highlights some cost minimization and effective utilization approaches to distribute resources efficiently to provide services in SGs with respect to LB algorithms. Three algorithms for Load balancing: throttled, round robin (RR) and particle swarm optimization (PSO) are compared in the paper. The comparative discussion and analysis of these algorithms are discussed with novel algorithm for load balancing in Smart Grids (SGs). Moreover, this paper also highlights some cost minimization and effective utilization approaches to distribute resource efficiently to services in SGs with respect to LB algorithms. In this article, we proposed analytical vision on the highly virtualized platform; Fog Computing (FC), an extension of cloud that provides computing, networking, storage, communication at the edge of the network and overwhelmed the existing challenges of SGs.
... In [33], Anila et al. proposed a load balancing algorithm known as particle swarm optimization with simulated annealing (PSOSA). The proposed algorithm is then compare with existing algorithms RR and Throttled (TH). ...
Demand Side Management (DSM) is an effective and robust scheme for energy management, Peak to Average Ratio (PAR) reduction and cost minimization. Many DSM techniques have been proposed for industrial, residential and commercial areas in last years. Smart Grid (SG) gives the opportunity of two-way digital communication to consumers and utility. SG balances and monitors the consumption of electricity of the consumer. Moreover, it reduces the cost and energy consumption of the utility and consumer. There are several Smart Cities (SCs) in the world. These SCs contain numerous Smart Societies (SSs) which have the number of Smart Buildings (SBs) contain Smart Homes (SHs). When requests from the consumer side sent to acquire the resources other storage issues also increase. To make an environment more efficient and enhance the performance of SG, cloud is introduced. Reducing delay and latency in the cloud computing environment is a challenging task for the research community. The resources are required to process and store data in cloud. To overcome these challenges, another infrastructure fog computing environment is introduced, which plays an important role to enhance the efficiency of the cloud. The Virtual Machines (VMs) are installed at fog to whom consumers’ requests are allocated. In this thesis, the cloud and fog based
integrated environment is proposed. The aim of this proposed environment is to overcome the delay and latency issues of cloud and to enhance the performance of fog. When there is a large number of incoming requests on fog and cloud, load balancing is another major issue. This issue is also been resolved in this thesis. The nature-inspired algorithms such as: Genetic Algorithm (GA), Crow Search Algorithm (CSA), Honey Bee (HB), Round Robin (RR), Particle Swarm Optimization (PSO) and Improved PSO by using Levy Walk (IPSOLW), Cuckoo Search (CS), CS with Levy distribution (CLW), BAT algorithm and Flower Pollination (FP) are proposed and implemented in this thesis. The aim of proposed GA and CSA is scheduling the load and minimizing the PAR and cost in SG environment. These algorithms also contribute in the cloud and fog based integrated environment of the thesis. To balance the load CSA, HB, IPSOLW, CLW, FP are proposed. The proposed algorithms are compared with implementing RR, PSO, and BAT. The comparative analysis of these proposed and implemented algorithms is done on the basis of service broker policies. The Closest Data Center (CDC), Optimize Response Time (ORT), Reconfigure Dynamically with load and proposed Advance Service Broker Policy (ASP) are also implemented in this thesis to evaluate the results of this thesis algorithm. On the basis of these policies, using aforementioned nature-inspired algorithms, the Response Time (RT), Processing Time (PT), VM cost, Data Transfer (DT) cost, Micro Grid (MG) cost and Total Cost (TC) is minimized in cloud and fog based integrated environment.
... One way of protecting the system in addition to use the resilient methods is using a smart, strong and secure framework for agents to exchange information in, rather than sharing information directly (which is the base of distributed optimization methods). Some related researches in this area are: [12] proposes a fog computing platform for energy management of microgrids and [13] suggests a platform based on cloud and fog for smart buildings. To the best of the authors' knowledge, a feasible fog-based platform does not exist to use distributed optimization methods in a virtual manner for microgrids. ...
... The maintenance cost of an MG is also called a recurring cost, which is proportional to the size of it. This paper is an extension of [25] and the contributions of this research are as follows: ...
The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.
... In SG, smart meter gathers information about energy consumption of users.The information gathered from smart meters are stored in cloud data centers. Multiple Scheduling algorithms are used to manage the load on the cloud and fog in [2]. Due to fog computing cloud computing is shifted at the edge of the network. ...
Load Balancing helps in minimizing the consumption of resources .Cloud and fog concept is used to manage these resources. As the cloud is a centralized network it has information of all the customers. Fog is used to minimize the load on the cloud .The storage of cloud is permanent. However, fog has temporary storage. Smart Grid(SG) technology presents an opportunity that improves reliability, efficiency and stainability. Fog is used to reduce the load on the cloud. In this paper an effective fog and cloud based environment for energy management of resources is proposed. It handles the data of clusters of buildings at the user-end. Each cluster of buildings has the multiple number of apartments. Six fogs are considered for six different regions. Six number of clusters are considered in this scenario. Each cluster has one fog. MicroGrids (MG) are available near the buildings and accessible by fog. Multiple algorithms are used for load balancing to manage the load. The proposed algorithm in this scenario is Min-Min algorithm. The Min-Min algorithm is a simple algorithm that manages the resources efficiently. In this algorithm the completion time of a task is calculated and initially, resources are allocated to those tasks which have minimum execution time. Results are compared with Round Robin(RR) algorithm which is also used for load balancing. Simulation results shows that by applying the proposed algorithm the cost is reduced as compare to RR.
... Authors proposed three-layered based clod-fog architecture in [13]. Three load balancing algorithms throttled, RR, and Particle Swarm Optimization with Simulated Annealing (PSOSA) are used for fog resource allocation. ...
Smart grid (SG) provides observable energy distribution where utility and consumers are enabled to control and monitor their production , consumption, and pricing in almost, real time. Due to increase in the number of smart devices complexity of SG increases. To overcome these problems, this paper proposes cloud-fog based SG paradigm. The proposed model comprises three layers: cloud layer, fog layer, and end user layer. The 1st layer consists of the cluster of buildings. The renewable energy source is installed in each building so that buildings become self-sustainable with respect to the generation and consumption. The second layer is fog layer which manages the user's requests, network resources and acts as a middle layer between end users and cloud. Fog creates virtual machines to process multiple users request simultaneously, which increases the overall performance of the communication system. MG is connected with the fogs to fulfill the energy requirement of users. The top layer is cloud layer. All the fogs are connected with a central cloud. Cloud provides services to end users by itself or through the fog. For efficient allocation of fog resources, artificial bee colony (ABC) load balancing algorithm is proposed. Finally, simulation is done to compare the performance of ABC with three other load balancing algorithms, particle swarm optimization (PSO), round robin (RR) and throttled. While considering the proposed scenario, results of these algorithms are compared and it is concluded that performance of ABC is better than RR, PSO and throttled.
... In [10], Anila et al. proposed a load balancing algorithm known as particle swarm optimization with simulated annealing (PSOSA). The proposed algorithm is then compare with existing algorithms RR and Throttled (TH). ...
In this paper, Smart Grid (SG) efficiency is improved by introducing Cloud-based environment. To access the services and hostage of cloud large number of requests are entertained from Smart Homes (SHs). These SHs exists in clusters of smart buildings. When the number of requests increases, delay, latency and response time also increase. To overcome these issues, Fog is introduced, which act as an intermediate layer between the cloud and consumer. Five Micro Grids (MGs) are attached to each cluster of the smart building to manage its requests. By using Fog base environment, the delay and latency decrease. The response time also increases with less processing time. To handle the load on cloud different load balancing algorithms and service broker policies exist. In order to manage the load, Honey Bee (HB) is implemented. HB is compared with existing algorithm Round Robin (RR). It gives better results than RR.
... A load balancing algorithm known as particle swarm optimization with simulated annealing (PSOSA) is proposed by Anila et al. in [6]. Additionally, they also proposed a three layer model for cloud and fog computing environment. ...
The concept of cloud computing is becoming popular with each passing day. Clouds provide virtual environment for computation and storage. Number of cloud users is increasing drastically which may cause network congestion problem. To avoid such situation, fog computing is used along with cloud computing. Cloud act as a global system and fog works locally. As the requests from users are increasing so load balancing is also required on fog side. In this paper, a three layered cloud and fog based architecture are proposed. Fog computing acts as a middle layer between users and the cloud. Users requests are handled at fog layer and filtered data is forwarded to cloud. A single fog has multiple virtual machines (VMs) that are assigned to the users requests. The load balancing problem of these requests is managed by proposed weighted cuckoo search (WCS) algorithm. Simulations are carried out to evaluate the performance of the proposed model. Results are presented in the form of bar graphs for comparison and detailed values of each parameter are presented in tables. Results show the effectiveness of the proposed technique.
Cloud computing is an up-to-date model for distributing information processing utility and provides a large amount of resources through the internet. The major challenges affecting a cloud computing environment include resource provisioning and security. In this paper, we focused on resource provisioning mechanisms using Meta-heuristics techniques such as spider monkey optimization (SMO) and simulated annealing (SA). A simulated annealing algorithm helps to give a fine solution along with statistical promises for uncovering the best solution, yet it cannot notify whether the best solution is found. So it requires another method to overcome this drawback. This paper presents the Spider Monkey Optimization algorithm with Simulated Annealing (SMO-SA) to generate the best fitness value possible. The aim of the proposed hybrid algorithm is to generate the minimum fitness value by combining spider monkey optimization with simulated annealing to provision the resources dynamically. This paper also presents the step-by-step mathematical working of our proposed hybrid algorithm by applying it to the relevant data set and calculating the speedup factor as well as mean square error (MSE) value along with fitness value, which shows the effective impact of our proposed SMO-SA algorithm.
Smart Grid benefits from information and communications technology (ICT) to integrate data from different sources across the network. The growing market for electric vehicles (EV) and electric devices is increasing energy demand. To manage a large amount of complex intelligent equipment, EV charging and discharging, and data-intensive devices, a reliable modern smart grid with a service-oriented, secure, efficient, and cost-effective system is compelling. Cloud computing is a promising approach to achieve that objective as monitoring power grid data flow and data center management are the key. This paper proposes an optimization model for energy management within the power cloud. Our energy system model enables accessible services to computing resources and real-time data stream processing within an integrated environment. Using digital technology, the proper implementation of our model reduces costs and energy consumption and improves the smart grid reliability for customers and energy providers in a distributed manner. This paper presents the implementation of a three-procedure optimization algorithm within a novel Multi-Agent Cloud-fog Structure (MACS) to meet the requirements raised by smart grid communication and distribution. Our model promotes reliable energy consumption adjustments by end-users who can choose power supplied by solar, wind, geothermal, biomass, or other renewable sources or from non-renewable sources in ways that reveal opportunities for demand-supply balance and energy saving.
Cloud computing is new technology that has considerably changed human life at different aspect over the last decade. Especially after the COVID-19 pandemic, almost all life activity shifted into cloud base. Cloud computing is a utility where different hardware and software resources are accessed on pay per user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization.VM used in data center for distribution of resource and application according to benefactor demand. Cloud data center faces different issue in respect of performance and efficiency for improvement of these issues different approaches are used. Virtual machine play important role for improvement of data center performance therefore different approach are used for improvement of virtual machine efficiency (i-e) load balancing of resource and task. For the improvement of this section different parameter of VM improve like makespan, quality of service, energy, data accuracy and network utilization. Improvement of different parameter in VM directly improve the performance of cloud computing. Therefore, we conducting this review paper that we can discuss about various improvements that took place in VM from 2015 to 20,201. This review paper also contain information about various parameter of cloud computing and final section of paper present the role of machine learning algorithm in VM as well load balancing approach along with the future direction of VM in cloud data center.
The way to organize the generation, storage, and management of renewable energy and energy consumption features has taken relevance in recent years due to demands that define the social welfare of this century. Like demand increases, other factors require grid infrastructure improvement, updates, and opening to other technologies that assuage the final customer needs. Precisely, the interest in renewable energy sources, the constant evolution of energy storage technologies, the continuous research involving microgrid management systems, and the evolution of cloud computing technologies and machine learning strategies motivate the development of this article. Tasks associated with a microgrid cluster like the integration of a considerable number of heterogeneous devices, real-time support, information processing, massive storage capabilities, security considerations, and advanced optimization techniques usage could take place in an autonomous and scalable energy management system architecture under a machine learning perspective running in real-time and using Cloud resources. This paper focuses on identifying the elements considered by different authors to define a cloud-based architecture and ensure the appropriately supervised learning functionality under a microgrids cluster environment. Namely, it was necessary to revise and run microgrid simulations, real-time simulation platforms usage, connection to a virtual server for microgrid control and set the energy management system using cloud computing and machine learning. Based on the review and considering the scenarios mentioned, this article presents a scalable and autonomous cloud-based architecture that allows power generation forecast, energy consumption prediction, a real-time energy management system using machine learning techniques.
Recently, distributed cloud infrastructures have become a potential business opportunity for most service providers due to the exponential growth of connected devices. The advent of the Internet of Things (IoT) and softwarized networks made centralized cloud systems impractical. In response, Fog Computing (FC) emerged, enabling the deployment of services on computational resources from the cloud up to the edge. However, the adoption of FC concepts is still in its early stages and challenges persist to fully benefit from fog–cloud infrastructures. One of them is known as Service Function Chaining (SFC) where providers benefit from network softwarization to create virtual chains of connected services. Recent research has tackled SFC allocation through theoretical modeling and heuristic algorithms, which often cannot cope with the dynamic behavior of the network. Thus, in this chapter, we explore a subset of machine learning (ML) called Reinforcement Learning (RL) to provide an efficient solution for SFC allocation in FC. The proposed approach learns about the best resource allocation decisions, focused on energy efficiency from a previously presented mixed-integer linear programming (MILP) formulation. Results showed that RL algorithms perform comparably to state-of-the-art ILP-based implementations while offering more scalable solutions. Future research directions and open challenges are discussed.
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.
In this paper, a new distributed multi-agent framework based on the three layers’ fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.
Load Balancing helps in minimizing the consumption of resources. Cloud and fog concept is used to manage these resources. As the cloud is a centralized network it has information of all the customers. Fog is used to minimize the load on the cloud. The storage of cloud is permanent. However, fog has temporary storage. Smart Grid (SG) technology presents an opportunity that improves reliability, efficiency and stainability. Fog is used to reduce the load on the cloud. In this paper an effective fog and cloud based environment for energy management of resources is proposed. It handles the data of clusters of buildings at the user-end. Each cluster of buildings has the multiple number of apartments. Six fogs are considered for six different regions. Six number of clusters are considered in this scenario. Each cluster has one fog. MicroGrids (MG) are available near the buildings and accessible by fog. Multiple algorithms are used for load balancing to manage the load. The proposed algorithm in this scenario is Min-Min algorithm. The Min-Min algorithm is a simple algorithm that manages the resources efficiently. In this algorithm the completion time of a task is calculated and initially, resources are allocated to those tasks which have minimum execution time. Results are compared with Round Robin (RR) algorithm which is also used for load balancing. Simulation results shows that by applying the proposed algorithm the cost is reduced as compare to RR.
Minimizing the electricity consumption and cost is one of the most demanding needs of today. As with the rapid increase in demand, there is a great need to design new solutions for effective energy management. With the advent of new Information Communication Technologies (ICT) traditional electricity grids, meters, buildings and appliances became Smart Grids (SGs), Smart Meters (SMs), Smart Buildings (SBs) and Smart Appliances (SAs). The SBs consists of a large number of SAs. These smart appliances are constantly sharing, their data with SGs, SMs and SBs. So a huge amount of data is generated every day. This data requires complex computations, faster retrievals and larger storage facilities [1]. Keeping this in view, a new energy management system is designed with the help of Cloud and Fog computing. As the Cloud Computing (CC) provides large number of data computation and permanent storage facilities however, it has limitations in fast data retrieval and causes response delays. On the other hand, Fog Computing (FC) offers faster information retrieval with less response delays with only limitation of temporary storage. The proposed system architecture integrates the qualities of both CC and FC by combining their services. To manage the load between different Virtual Machines (VMs) on Fog servers a new load balancing algorithm Modified Shortest Job First (MSJF) is the proposed. The performance of proposed algorithm is evaluated through different performance parameters. e.g. Processing Time (PT), Response Time (RT) and cost. To validate the performance of proposed scheme simulations are carried out in the Cloud Analyst tool. From the results it is assumed that the proposed technique can not outperforms the Round Robin (RR) and Throttled algorithms, due to its limitations in network delays and RT.
Smart grid (SG) provides observable energy distribution where utility and consumers are enabled to control and monitor their production, consumption, and pricing in almost, real time. Due to increase in the number of smart devices complexity of SG increases. To overcome these problems, this paper proposes cloud-fog based SG paradigm. The proposed model comprises three layers: cloud layer, fog layer, and end user layer. The 1st layer consists of the cluster of buildings. The renewable energy source is installed in each building so that buildings become self-sustainable with respect to the generation and consumption. The second layer is fog layer which manages the user’s requests, network resources and acts as a middle layer between end users and cloud. Fog creates virtual machines to process multiple users request simultaneously, which increases the overall performance of the communication system. MG is connected with the fogs to fulfill the energy requirement of users. The top layer is cloud layer. All the fogs are connected with a central cloud. Cloud provides services to end users by itself or through the fog. For efficient allocation of fog resources, artificial bee colony (ABC) load balancing algorithm is proposed. Finally, simulation is done to compare the performance of ABC with three other load balancing algorithms, particle swarm optimization (PSO), round robin (RR) and throttled. While considering the proposed scenario, results of these algorithms are compared and it is concluded that performance of ABC is better than RR, PSO and throttled.
The integration of Smart Grid (SG) with cloud and fog computing has improved the energy management system. The conversion of traditional grid system to SG with cloud environment results in enormous amount of data at the data centers. Rapid increase in the automated environment has increased the demand of cloud computing. Cloud computing provides services at the low cost and with better efficiency. Although problems still exists in cloud computing such as Response Time (RT), Processing Time (PT) and resource management. More users are being attracted towards cloud computing which is resulting in more energy consumption. Fog computing is emerged as an extension of cloud computing and have added more services to the cloud computing like security, latency and load traffic minimization. In this paper a Cuckoo Optimization Algorithm (COA) based load balancing technique is proposed for better management of resources. The COA is used to assign suitable tasks to Virtual Machines (VMs). The algorithm detects under and over utilized VMs and switch off the under-utilized VMs. This process turn down many VMs which puts a big impact on energy consumption. The simulation is done in Cloud Sim environment, it shows that proposed technique has better response time at low cost than other existing load balancing algorithms like Round Robin (RR) and Throttled.
The mathematical properties of the spatial movement patterns of animals, humans, and insects have gradually become clear in recent years. Motion tracking is essentially necessary for the study of the spatial movement patterns. GPS telemetry is often used for large mammals, birds, and humans. However, it is difficult to track the migration paths of insects and small fish by using GPS telemetry. When the region of an object’s movement is restricted, we can record the movement of the object by the video instead of GPS telemetry, but we must determine the position coordinates of objects from a video for the study of the spatial movement patterns. If this motion tracking can be executed not by manually but automatically, we can obtain and analyze Big data on motion. In this paper, we develop a system for motion tracking of one or more small fishes in an aquarium. This system solves the difficulties such as the overlap of fishes, the ghost image of the reflection, and outputs the trajectory of the 3-dimensional coordinates. Furthermore, we apply this system to actual videos and show that the detection of active and inactive phases is possible and that the spatial movement pattern follows Levy walk.
Cloud computing is major component in our daily life; Integration of Cloud with smart grid brings an important role in electricity management. Fog computing concept is also introduced in this paper which helps to minimize the load on cloud. Many techniques are introduced in papers that includes Round Robin (RR), Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) etc. In this paper authors introduce First Come First Serve (FCFS) load balancing technique with the broker policy of Closest Data Center to allocate resources for Virtual Machines (VM). FCFS algorithm results are compared with existing known algorithms which includes RR and Throttled algorithm. The Response Time (RT) is less in some clusters as compared to RR and Throttled algorithm. The main goal is to optimise the Response Time (RT) on cloud.
In this article, a resource allocation model is presented in order to optimize the resources in residential buildings. The whole world is categorized into six regions depending on its continents. The fog helps cloud computing connectivity on the edge network. It also saves data temporarily and sends to the cloud for permanent storage. Each continent has one fog which deals with three clusters having 100 buildings. Microgrids (MGs) are used for the effective electricity distribution among the consumers. The control parameters considered in this paper are: clusters, number of buildings, number of homes and load requests whereas the performance parameters are: cost, Response Time (RT) and Processing Time (PT). Particle Swarm Optimization with Simulated Annealing (PSOSA) is used for load balancing of Virtual Machines (VMs) using multiple service broker policies. Service broker policies in this paper are: new dynamic service proximity, new dynamic response time and enhanced new response time. The results of proposed service broker policies with PSOSA are compared with the existing policy: new dynamic service proximity. New dynamic response time and enhanced new dynamic response time performs better than the existing policy in terms of cost, RT and PT. However, the maximum RT and PT of proposed policies is more than the existing policy. We have used CloudAnalyst for conducting simulations for the proposed scheme.
The concept of cloud computing is becoming popular with each passing day. Clouds provide virtual environment for computation and storage. Number of cloud users is increasing drastically which may cause network congestion problem. To avoid such situation, fog computing is used along with cloud computing. Cloud act as a global system and fog works locally. As the requests from users are increasing so load balancing is also required on fog side. In this paper, a three layered cloud and fog based architecture is proposed. Fog computing acts as a middle layer between users and the cloud. Users’ requests are handled at fog layer and filtered data is forwarded to cloud. A single fog has multiple virtual machines (VMs) that are assigned to the users’ requests. The load balancing problem of these requests is managed by proposed weighted cuckoo search (WCS) algorithm. Simulations are carried out to evaluate the performance of proposed model. Results are presented in the form of bar graphs for comparison and detailed values of each parameter are presented in tables. Results show the effectiveness of proposed technique.
In this paper, Smart Grid (SG) efficiency is improved by introducing Cloud-based environment. To access the services and hostage of cloud large number of requests are entertained from Smart Homes (SHs). These SHs exists in clusters of smart buildings. When the number of requests increase, delay, latency and response time also increase. To overcome these issues, Fog is introduced, which act as an intermediate layer between the cloud and consumer. Five Micro Grids (MGs) are attached to each cluster of the smart building to manage its requests. By using Fog base environment, the delay and latency decreases. The response time also increases with less processing time. To handle the load on cloud different load balancing algorithms and service broker policies exist. In order to manage the load, Honey Bee (HB) is implemented. HB is compared with existing algorithm Round Robin (RR). It gives better results than RR.
Due to unfolded developments in both the IT sectors viz. Intelligent Transportation and Information Technology contemporary Smart Grid (SG) systems are leveraged with smart devices and entities. Such infrastructures when bestowed with the Internet of Things (IoT) and sensor network make a universe of objects active and online. The traditional cloud deployment succumbs to meet the analytics and computational exigencies decentralized, dynamic cum resource-time critical SG ecosystems. This paper synoptically inspects to what extent the cloud computing utilities can satisfy the mission-critical requirements of SG ecosystems and which subdomains and services call for fog based computing archetypes. The objective of this work is to comprehend the applicability of fog computing algorithms to interplay with the core centered cloud computing support, thus enabling to come up with a new breed of real-time and latency free SG services. The work also highlights the opportunities brought by fog based SG deployments. Correspondingly, we also highlight the challenges and research thrusts elucidated towards the viability of fog computing for successful SG Transition.
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.
This paper proposes a cloud computing framework in smart grid environment by creating small integrated energy hub supporting real time computing for handling huge storage of data. A stochastic programming approach model is developed with cloud computing scheme for effective Demand Side Management (DSM) in smart grid. Simulation results are obtained using GUI interface and Gurobi optimizer in Matlab in order to reduce the electricity demand by creating energy networks in a smart hub approach.
This paper focuses on the procurement of load shifting service by optimally scheduling the charging and discharging of PEVs in a decentralized fashion. We assume that the energy flow between PEVs and the grid is bidirectional, i.e., PEVs can also release energy back into the grid as distributed generation, which is known as vehicle-to-grid (V2G). The optimal scheduling problem is then formulated as a mixed discrete programming (MDP) problem, which is NP-hard and extremely difficult to solve directly. To get over this difficulty, we propose a solvable approximation of the MDP problem by exploiting the shape feature of the base demand curve during the night, and develop a decentralized algorithm based on iterative water-filling. Our algorithm is decentralized in the sense that the PEVs compute locally and communicate with an aggregator. The advantages of our algorithm include reduction in computational burden and privacy preserving. Simulation results are given to show the performance of our algorithm.
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 fast-paced development of power systems necessitates smart grids to facilitate real-time control and monitoring with bidirectional communication and electricity flows. Future smart grids are expected to have reliable, efficient, secured, and cost-effective
power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas — energy management,
information management, and security. In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid. We also highlight different challenges existing in the conventional smart grid (without cloud application) that can be overcome using cloud. In this survey, we present a synthesized overview of the current state of research on smart grid development. We also identify the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.
Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of Electric Vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for smart grid and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for smart grid based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging-discharging loads at peak-hours periods.
By locally solving an optimization problem and broadcasting an update message over the underlying communication infrastructure, demand response program based on the distributed optimization model encourage all users to participate in the program. However, some challenging issues present themselves, such as the existence of an ideal communication network, especially when utilizing wireless communication, and the effects of communication channel properties, like the bit error rate, on the overall performance of the demand response program. To address the issues, this paper first defines a Cloud-based Demand Response (CDR) model, which is implemented as a two-tier cloud computing platform. Then a communication model is proposed to evaluate the communication performance of both the CDR and DDR (Distributed Demand Response) models. The present study shows that when users are finely clustered, the channel bit error rate is high and the User Datagram Protocol (UDP) is leveraged to broadcast the update messages, making the optimal solution unachievable. Contradictory to UDP, the Transmission Control Protocol (TCP) will be caught up with a higher bandwidth and increase the delay in the convergence time. Finally, the current work presents a cost-effectiveness analysis which confirms that achieving higher demand response performance incurs a higher communication cost.
Energy is one of the most valuable resources of the modern era and needs to be consumed in an optimized manner by an intelligent usage of various smart devices, which are major sources of energy consumption nowadays. With the popularity of low-voltage DC appliances such as-LEDs, computers, and laptops, there arises a need to design new solutions for self-sustainable smart energy buildings containing these appliances. These smart buildings constitute the next generation smart cities. Keeping focus on these points, this article proposes a cloud-assisted DC nanogrid for self-sustainable smart buildings in next generation smart cities. As there may be a large number of such smart buildings in different smart cities in the near future, a huge amount of data with respect to demand and generation of electricity is expected to be generated from all such buildings. This data would be of heterogeneous types as it would be generated from different types of appliances in these smart buildings. To handle this situation, we have used a cloudbased infrastructure to make intelligent decisions with respect to the energy usage of various appliances. This results in an uninterrupted DC power supply to all low-voltage DC appliances with minimal dependence on the grid. Hence, the extra burden on the main grid in peak hours is reduced as buildings in smart cities would be self-sustainable with respect to their energy demands. In the proposed solution, a collection of smart buildings in a smart city is taken for experimental study controlled by different data centers managed by different utilities. These data centers are used to generate regular alerts on the excessive usage of energy from the end users' appliances. All such data centers across different smart cities are connected to the cloud-based infrastructure, which is the overall manager for making all the decisions about energy automation in smart cities. The efficacy of the proposed scheme is evaluated with respect to various performance evaluation metrics such as satisfaction ratio, delay incurred, overhead generated, and demand-supply gap. With respect to these metrics, the performance of the proposed scheme is found to be good for implementation in a realworld scenario.
Demand Side Management (DSM) is an important application of the future Smart Grid (SG). DSM programs allow consumers to participate in the operation of the electric grid by reducing or shifting their electricity usage during peak periods. Therefore, in this paper we propose a two-tier cloud-based demand side management to control the residential load of customers equipped with local power generation and storage facilities as auxiliary sources of energy. We consider a power system consisting of multiple regions and equipped with a number of microgrids. In each region an edge cloud is utilized to find the optimal power consumption schedule for customer appliances in that region. We propose a two-level optimization algorithm with a linear multi-level cost function. At the edge cloud, the power consumption level of local storage and the amount of power being demanded from both local storage facilities and power grid are scheduled using a bi-level optimization approach. The core cloud then gathers information of the total demand from consumers in different regions and finds the optimal power consumption schedule for each microgrid in the power system. Simulation results show that the proposed model reduces consumption cost for the customers and improves the power grid in terms of peak load and peak-to-average load ratio.
With the rapid increase of monitoring devices and controllable facilities in the demand side of electricity networks, more solid information and communication technology (ICT) resources are required to support the development of demand side management (DSM). Different from traditional computation in power systems which customizes ICT resources for mapping applications separately, DSM especially asks for scalability and economic efficiency, because there are more and more stakeholders participating in the computation process. This paper proposes a novel cost-oriented optimization model for a cloud-based ICT infrastructure to allocate cloud computing resources in a flexible and cost-efficient way. Uncertain factors including imprecise computation load prediction and unavailability of computing instances can also be considered in the proposed model. A modified priority list algorithm is specially developed in order to efficiently solve the proposed optimization model and compared with the mature simulating annealing based algorithm. Comprehensive numerical studies are fulfilled to demonstrate the effectiveness of the proposed cost-oriented model on reducing the operation cost of cloud platform in DSM.
In the context of electricity market and smart grid, the uncertainty of electricity prices due to the high complexities involved in market operation would significantly affect the profit and behavior of electric vehicle (EV) aggregators. An information gap decision theory-based approach is proposed in this paper to manage the revenue risk of the EV aggregator caused by the information gap between the forecasted and actual electricity prices. The proposed decision-making framework can offer effective strategies to either guarantee the predefined profit for risk-averse decision-makers or pursue the windfall return for risk-seeking decision-makers. Day-ahead charging and discharging scheduling strategies of the EV aggregators are arranged using the proposed model considering the risks introduced by the electricity price uncertainty. The results of case studies validate the effectiveness of the proposed framework under various price uncertainties.
By introducing microgrids, energy management is required to control the power generation and consumption for residential, industrial, and commercial domains, e.g., in residential microgrids and homes. Energy management may also help us to reach zero net energy (ZNE) for the residential domain. Improvement in technology, cost, and feature size has enabled devices everywhere, to be connected and interactive, as it is called Internet of Things (IoT). The increasing complexity and data, due to the growing number of devices like sensors and actuators, require powerful computing resources, which may be provided by cloud computing. However, scalability has become the potential issue in cloud computing. In this paper, fog computing is introduced as a novel platform for energy management. The scalability, adaptability, and open source software/hardware featured in the proposed platform enable the user to implement the energy management with the customized control-as-services, while minimizing the implementation cost and time-to-market. To demonstrate the energy management-as-a-service over fog computing platform in different domains, two prototypes of home energy management (HEM) and microgrid-level energy management have been implemented and experimented.
The smartphone is a typical cyberphysical system (CPS). It must be low energy consuming and highly reliable to deal with the simple but frequent interactions with the cloud, which constitutes the cloud-integrated CPS. Dynamic voltage scaling (DVS) has emerged as a critical technique to leverage power management by lowering the supply voltage andfrequency of processors. In this paper, based on the DVS technique, we propose a novel Energy-aware Dynamic Task Scheduling (EDTS) algorithm to minimize the total energy consumption for smartphones, while satisfying stringent time constraints and the probability constraint for applications. Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for CPS, as compared to the critical path scheduling method and the parallelism-based scheduling algorithm.
GridSpice: A distributed simulation platform for the smart grid
- Kyle Anderson
- Jimmy Du
- Amit Narayan
- Abbas El Gamal
Anderson, Kyle, Jimmy Du, Amit Narayan, and Abbas El Gamal.
"GridSpice: A distributed simulation platform for the smart grid." IEEE
Transactions on Industrial Informatics 10, no. 4 (2014): 2354-2363.
Power Consumption Scheduling for Future Connected Smart Homes Using Bi-Level Cost-Wise Optimization Approach
- Yaghmaee Moghaddam
- Mohammad Hossein
- Morteza Moghaddassian
- Alberto Leon-Garcia
Yaghmaee Moghaddam, Mohammad Hossein, Morteza Moghaddassian,
and Alberto Leon-Garcia. "Power Consumption Scheduling for Future
Connected Smart Homes Using Bi-Level Cost-Wise Optimization Approach." Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering 166 (2016).
On the performance of distributed and cloudbased demand response in smart grid
- Mohammad Moghaddam
- Alberto Hossein Yaghmaee
- Morteza Leongarcia
- Moghaddassian
On the performance of distributed and cloudbased demand response in smart grid
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Power Consumption Scheduling for Future Connected Smart Homes Using Bi-Level Cost-Wise Optimization Approach
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