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Efficient Energy Management Assisted by Fog Computing
Abstract
Now a day, every field is consuming services of Cloud Computing (CC) because everyone is not able to deploy his own data centers. Due to the large number of requests on central place concept of Fog for the distributed processing of requests. Fog is placed between user and cloud to reduce the Response Time (RT). Smart Grid (SG) is also integrated cloud architecture for efficient management of energy. In this paper Fog Based architecture for SG is presented. Due to this there is no chance of a peak time generation at any time. A three-tier architecture is used for request processing. In SG environment there is need of low latency and high RT for providing uninter-pretable services to the end user. For this purpose, Fog layer is used between cloud and SM (Smart Meter) and it acts as intermediate layer. A model is presented in this paper for efficient distribution of load among all available Virtual Machines (VMs). A load balancing technique is implemented for load distribution. Two regions are taken into account for experimental results and each region is divided into six group/cluster of building. Three Fogs are placed in each region for better RT and it produce optimal results as shown in simulations and discussion section.
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In this work, a new orchestration of Consumer to Fog to Cloud (C2F2C) based framework is proposed for efficiently managing the resources in residential buildings. C2F2C is a three layered framework consisting of cloud layer, fog layer and consumer layer. Cloud layer deals with on-demand delivery of the consumer’s demands. Resource management is intelligently done through the fog layer because it reduces the latency and enhances the reliability of cloud. Consumer layer is based on the residential users and their electricity demands from the six regions of the world. These regions are categorized on the bases of the continents. Two control parameters are considered: clusters of buildings and load requests, whereas four performance parameters are considered: Request Per Hour (RPH), Response Time (RT), Processing Time (PT) and cost in terms of Virtual Machines (VMs), Microgrids (MGs) and data transfer. These parameters are analysed by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. Two scenarios are used in the simulations: resource allocation using MGs and resource allocation using MGs and power storage devices for checking the effectiveness of the proposed work. The simulation results of the proposed technique show that it has outperformed the previous techniques in terms of the above-mentioned parameters. There exists a tradeoff in the PT and RT as compared to cost of VM, MG and data transfer.
We propose a novel network architecture by leveraging the cloudlet concept, the Software Defined Networking (SDN) technology, and the cellular network infrastructure to bring the computing resource to the mobile edge. In order to minimize the average response time for Mobile Users (MUs) in offloading their application workloads to the geographically distributed cloudlets, we propose the Latency awarE workloAd offloaDing (LEAD) strategy to allocate MUs’ application workloads into suitable cloudlets. Simulation results demonstrate that LEAD incurs the lowest average response time as compared to two other existing strategies.
Demand response is a key solution in smart grid to address the ever-increasing peak energy consumption. With multiple utility companies, users will decide from which utility company to buy electricity and how much to buy. Consequently, how to devise distributed real-time demand response in the multiseller–multibuyer environment emerges as a critical problem in future smart grid. In this paper, we focus on the real-time interactions among multiple utility companies and multiple users. We propose a distributed real-time demand response algorithm to determine each user's demand and each utility company's supply simultaneously. By applying dual decomposition, the original problem is firstly decoupled into single-seller–multibuyer subsystems; then, the demand response problem in each subsystem can be distributively solved. The major advantage of this approach is that each utility company and user locally solve subproblems to perform energy allocation, instead of requiring a central controller or any third party. Therefore, privacy is guaranteed because no entity needs to reveal or exchange private information. Numerical results are presented to verify efficiency and effectiveness of the proposed approach.
Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply–demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.
Fog computing concept is introduced to reduce the load on cloud and provide similar services as cloud. However, fog covers small area rather than cloud by storing the data temporarily and sends data to cloud for permanent storage. In this paper, an integrated fog and cloud based environment for effective energy management of buildings is proposed. So, the load on cloud and fog should be balanced. Various load balancing algorithms are used to manage the load among virtual machines (VMs). In this scenario, algorithm used for load balancing among VMs is round robin (RR). Service broker policies considered in this paper are; dynamically reconfigure with load (DR) and the proposed policy. New dynamic service proximity (DSP) service broker policy is proposed for fog selection and results of DSP policy are compared with DR policy. Therefore, a tradeoff is observed between cost and response time.
For constrained end devices in Internet of Things (IoT), such as smart meters, data transmission is an energy-consuming operation. To address this problem, we propose an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby smart meters and accurately derive aggregate statistics as the fine-grained Fog level aggregation. The Cloud/utility supplier computes overall aggregate statistics by aggregating Fog level aggregation. To minimize the privacy leakage and mitigate the utility loss, we use more efficient and concentrated Gaussian mechanism to distribute noise generation among parties, thus offering provable differential privacy guarantees of the aggregate statistic on both Fog level and Cloud level. In addition, to ensure aggregator obliviousness and system robustness, we put forward a two-layer encryption scheme: the first layer applies OTP to encrypt individual noisy measurement to achieve aggregator obliviousness, while the second layer uses public-key cryptography for authentication purpose. Our scheme is simple, efficient and practical, it requires only one round of data exchange among a smart meter, its connected Fog node and the Cloud if there are no node failures, otherwise, one extra round is needed between a meter, its connected Fog node and the trusted third party.
Edge cloudlets are promising to mitigate the high network delay incurred by the remote cloud in executing workloads offloaded from a user equipment (UE). However, the response time of a task request consists of both the network delay and computing delay. Considering the spatial and temporal dynamics of workloads among cloudlets, if the workload of an edge cloudlet is heavy, the computing delay in the cloudlet may be unbearable. In this letter, we design a hierarchical cloudlet network and propose a Workload ALLocation (WALL) scheme to minimize the average response time of UEs’ requests by deciding which cloudlet a UE is assigned to and how much computing resource is provisioned to serve it. The performance of the proposed scheme is validated by extensive simulations.
The continuing rollout of phasor measurement units (PMUs) enables wide area monitoring and control (WAMS/WACS), but the difficulty of sharing data in a secure, scalable, cost-effective, low-latency manner limits exploitation of this new capability by bulk electric power grid operators. GridCloud is an open-source platform for real time data acquisition and sharing across the jurisdictions that control a bulk interconnected grid. It leverages commercial cloud tools to reduce costs, employing cryptographic methods to protect sensitive data, and software-mediated redundancy to overcome failures. The system has been tested by ISO New England and the results reported here demonstrate a level of responsiveness, availability and security easily adequate for regional WAMS/WACS, with the capacity for nation-wide scalability in the future.
With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for businesses, entertainments and social interactions. This poses a great challenge of building a seamless application experience across different computing platforms. A key issue is the resource limitations of mobile devices due to their portable size, however this can be overcome by remotely executing computation-intensive tasks on clusters of near by computers called cloudlets. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks. In this paper we investigate how to balance the workload among multiple cloudlets in such a network to optimize mobile application performance. We introduce a novel system model to capture the response time delays of offloaded tasks and formulate an optimization problem with the aim to minimize the maximum response time among offloaded tasks. We then propose two algorithms for the problem. We finally evaluate the performance of the proposed algorithms in realistic simulation environments. The experimental results demonstrate the significant potential of the proposed algorithms in reducing response time, maximizing user experience.
To maximize task scheduling performance and minimize nonreasonable task allocation in clouds, this paper proposes a method based on a two-stage strategy. At the first stage, a job classifier motivated by a Bayes classifier's design principle is utilized to classify tasks based on historical scheduling data. A certain number of virtual machines (VMs) of different types are accordingly created. This can save time of creating VMs during task scheduling. At the second stage, tasks are matched with concrete VMs dynamically. Dynamic task scheduling algorithms are accordingly proposed. Experimental results show that they can effectively improve the cloud's scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods.
Fog computing could alleviate many of the Internet of Things' unique challenges. This special issue explores fog computing's opportunities and challenges to form a distributed and virtualized platform, supporting computation-intensive tasks and distributing advanced computing, storage, networking, and management services to the edge of the network. From reducing latency to enhancing security, this special issue delivers novel solutions to an exciting frontier.
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.
Demand response enabled by time-varying prices can propel the power industry toward a greater efficiency. However, a noncoordinated response of customers may lead to severe peak rebounds at periods with lower prices. In this regard, a coordinated demand response scheme can mitigate concerns about the peak rebounds. This paper presents a system-wide demand response management model to coordinate demand response provided by residential customers. The objective of the model is to flatten the total load profile that is subject to minimum individual cost of customers. The model is first formulated as a bi-level optimization problem. It is then casted into equivalent single-level problems, which are solved via an iterative distributed algorithm. Home load management (HLM) modules embedded in customers' smart meters are autonomous agents associated with the algorithm. In the algorithm, at first, HLM modules, in response to prices announced by the utility, optimize the daily operation of household appliances and send back the scheduled load profiles. Then, the total load profile is calculated and released by the utility. Thereafter, the HLM modules asynchronously update their schedule such that, given their least energy expenses, the most evenly distributed total load profile is achieved. The mutual interaction between the utility and HLM modules is continued to the point in which no further improvement is obtained. Convergence and optimality of the algorithm are proved.
http://cse.iitkgp.ac.in/~ayan.mondal/Paper/ENTRANT.pdf
Motivated by the potential ability of heating ventilation and air-conditioning (HVAC) systems in demand response (DR), we propose a distributed DR control strategy to dispatch the HVAC loads considering the current aggregated power supply (including the intermittent renewable power supply). The control objective is to reduce the variation of nonrenewable power demand without affecting the user-perceived quality of experience. To solve the problem, first, a queueing model is built for the thermal dynamics of the HVAC unit based on the equivalent thermal parameters (ETP) model. Second, optimization problems are formulated. Based on an extended Lyapunov optimization approach, a control algorithm is proposed to approximately solve the problems. Third, a DR control strategy with a low communication requirement is proposed to implement the control algorithm in a distributed way. Finally, practical data sets are used to evaluate and demonstrate the effectiveness and efficiency of the proposed control algorithm.
The future power grid expected to accommodate and integrates all new kinds of distributed renewable energy generations, intelligent energy management systems to accommodate the ever growing energy demand. This fact leads the trend of power grid to shift to the Smart Grid that uses advanced communication, smart meters, sensors and information technologies to create an automated, intelligent and widely distributed energy delivery network. A huge amount of row data is collected by smart meters and sensors from the end user and different part of the network to the computation system. Subsequently, this considerably big amount of data must be processed, analyzed and stored in a cost effective ways. In this manner, an enormous pool of computing resources and storage must be provided to compute this vast amount of data. Researchers have been suggesting different solutions. A distributed and parallel computing techniques for future power system, Grid computing [1], fast parallel processing of the mass data in cloud computing [2], and smart grid control software in cloud computing architecture [3]. This paper discussed the feasibility study of the handling of monitoring of renewable energy in smart grid on cloud computing framework retaining smart grid security, analysis of the availability of Energy management software tools in smart grid using cloud computing, design of a demonstrator and realization of demonstration for further education purpose to utilize the advantage of cloud computing distributed and scalable nature.
An intelligent cloudbased energy management system using machine to machine communications in future energy environments
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