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Green Fog: Cost Efficient Real Time Power Management Service for Green Community

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Abstract

The computing devices in cloud or fog data centers remain in the continuous running cycle to provide services. The long execution state of large number of computing devices consumes significant amount of power which emit equivalent amount of heat. The high powered cooling systems are installed for data centers to avoid reduced performance of the devices due to heat. In cloud based infrastructure, the longer response time between service provider and end-users is challenging for efficient power utilization in smart grid. In this paper, fog based model is proposed for a community to ensure real time energy management service provision with minimal network latency and efficient resource allocation technique. Moreover, a mechanism is proposed to calculate the energy demand for computing devices and cooling system for fog data center. In this paper, two scenarios are proposed to analyze the energy cost for a community and fog data center by integrating green power supply. The simulations show that green community with green fog is 15.09% more cost efficient as compared to the community with utility’s power supply only.

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Cloud datacenters are underutilized due to server over-provisioning. To increase datacenter utilization, cloud providers offer users an option to run workloads such as big data analytics on the underutilized resources, in the form of cheap yet revocable transient servers (e.g., EC2 spot instances, GCE preemptible instances). Though at highly reduced prices, deploying big data analytics on the unstable cloud transient servers can severely degrade the job performance due to instance revocations. To tackle this issue, this paper proposes iSpot, a cost-effective transient server provisioning framework for achieving predictable performance in the cloud, by focusing on Spark as a representative Directed Acyclic Graph (DAG)-style big data analytics workload. It first identifies the stable cloud transient servers during the job execution by devising an accurate Long Short-Term Memory (LSTM)-based price prediction method. Leveraging automatic job profiling and the acquired DAG information of stages, we further build an analytical performance model and present a lightweight critical data checkpointing mechanism for Spark, to enable our design of iSpot provisioning strategy for guaranteeing the job performance on stable transient servers. Extensive prototype experiments on both EC2 spot instances and GCE preemptible instances demonstrate that, iSpot is able to guarantee the performance of big data analytics running on cloud transient servers while reducing the job budget by up to 83.8% in comparison to the state-of-the-art server provisioning strategies, yet with acceptable runtime overhead.
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Recent challenges in power system stability and operation are due to the fact that these complex systems have not evolved in a way to deal with new forms of power generation and load types. Although the grid of the twenty-first century known as “Smart Grid” uses technologies such as two-way communication, advanced sensors, computer-based remote control, and automation, it does not adequately consider increased use of renewable energy which is becoming a major component of the power grid. Moreover, the potential for instability caused by increased frequency deviation and energy imbalance due to high penetration of Renewable Energy Sources (RES) places major constraints on their usage. Therefore, finding a solution to improve the stability of power systems with high penetration of Renewable Energy (RE) is a major challenge that needs to be addressed.
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Feed-in tariff and renewable portfolio standards are two major policies for renewable power generation and China is the first country to adopt both policies at the national level. The black box for regional allocation of renewable portfolio standards, however, may lead to the instability and inefficiency of this policy. This paper introduces the principles of Management by Objective to build a clear policy framework for renewable portfolio standards in China and employs the entropy method to equally and reasonably assign regional responsibility for renewable energy development. A comparison is made between our allocation and the goal-directed policy to determine whether historical responsibility and infrastructure construction should be further considered in crafting renewable portfolio standards. The trends for regional renewable quotas are influenced by the twin-track approach of nearby utilization and external transmission. Finally, based on our analysis, the policy implications behind the new incentive are presented to urge the establishments of supporting facilities.
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Workflows are a set of tasks and the dependency among them, which are divided into scientific and business categories. To avoid problems of centralized execution of workflows, they are broken into segments that is known as fragmentation. To fragment the workflow, it is highly important to consider the dependency among tasks and runtime conditions. The cooperation between the scheduler and fragmentor must be such that the latter generates appropriate tasks with optimized communication cost, delay time, response time, and throughput. To this end, in the present study, a framework is proposed for scheduling and fragmentation of tasks in scientific workflows that are conducted in fragmentation and scheduling phases. In the fragmentation phase, the fragments are generated with regard to the number of virtual machines available during runtime. In the scheduling phase, the virtual machines are selected with the aim of reducing bandwidth usage. The experiments are performed with three Configurations during both phases of fragmentation and scheduling. Response time, throughput, and cost (BW and RAM) were improved compared to the baseline studies on Sipht, Inspiral, Epigenomics, Montage, and CyberShake workflows as datasets.
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This paper proposes a real-time energy management system (EMS) suitable for rooftop PV installations with battery storage. The EMS is connected to a smart grid where the price signals indirectly control the power output of the PV/battery system in response to the demand variation of the electricity networks. The objective of the EMS is to maximize the revenue over a given time period while meeting the battery stored energy constraint. The optimization problem is solved using the method of Lagrange multipliers. The uniqueness of the proposed EMS remains in the reactive real-time control mechanism that compensates for the PV power forecast error. The proposed EMS requires only forecasting the average PV power output over the total optimization period. This is in contrast to the predictive power scheduling techniques that require accurate instantaneous PV power forecast. The proposed EMS method is verified by benchmarking against the predictive brute-force dynamic programming (DP) approach. The simulation analysis considers days with varying solar irradiance profiles. The simulation analysis shows the proposed EMS operating under practical assumptions, where the battery storage capacity is subject to constraints and the PV power output is not known a priori.
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In the age of big data, companies tend to deploy their services in data centers rather than their own servers. The demands of big data analytics grow significantly, which leads to an extremely high electricity consumption at data centers. In this paper, we investigate the cost minimization problem of big data analytics on geo-distributed data centers connected to renewable energy sources with unpredictable capacity. To solve this problem, we propose a Reinforcement Learning (RL) based job scheduling algorithm by combining RL with neural network (NN). Moreover, two techniques are developed to enhance the performance of our proposal. Specifically, Random Pool Sampling (RPS) is proposed to retrain the NN via accumulated training data, and a novel Unidirectional Bridge Network (UBN) structure is designed for further enhancing the training speed by using the historical knowledge stored in the trained NN. Experiment results on real Google cluster traces and electricity price from Energy Information Administration show that our approach is able to reduce the data centers' cost significantly compared with other benchmark algorithms.
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Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or health care industry required rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in fog server plays key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a trade-off is maintained between the energy consumption and makespan. Specifically, this paper propose a metaheuristic-based service allocation framework using three metaheuristic techniques, such as Particle Swarm Optimization (PSO), Binary PSO (BPSO) and Bat algorithm (BAT). These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic based service allocation algorithms by conducting a set of rigorous evaluations.
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Data centers (DC) are richly instrumented systems consists of highly coupled elements that store and process a large amount of data. To perform large computation and storage, a DC is equipped with more than thousands of servers or even more. Due to a large number of these computational devices put in use at DC, produces a large amount of heat. Therefore the cost to maintain the thermal balance in a DC has increased significantly and has become almost equal to the cost of operating these systems. The main problem in heat management is ‘Hot spots creation’ which can cause hardware inefficiencies, and operational disruptions and in turn have a negative impact on overall functionality.
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Cooling system plays a key role in modern data center. Developing an optimal control policy for data center cooling system is a challenging task. The prevailing approaches often rely on approximated system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to sub-optimal or unstable performances. In this paper we propose to utilize the large amount of monitoring data in data center to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tab it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-to-end neural control algorithm that is based on the actor-critic framework and the deep deterministic policy gradient (DDPG) technique. To improve the robustness of the control algorithm, we test various DRL related optimization techniques, such as recurrent decision making, discounted return, different neural network architectures, and different stochastic gradient descent algorithms, and adding additional constraints on the output of the policy network. We evaluate the proposed algorithms on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed end-to-end cooling control algorithm can achieve about 10% cooling cost saving on the simulation platform compared with a canonical two stage optimization algorithm; and it can achieve about 13.6% cooling energy saving on the NSCC data trace. Furthermore, it shows high accuracy in predicting the temperature of the racks (with mean absolute error 0.1 degree) and can control the temperature of the data center zone close to the predefined threshold with variation lower to 0.2 degree.
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The Feed-in Tariff (FIT) has been successfully used to promote the development of renewable energy; nevertheless, it may cause financial burden on the governments at the same time. Compared with FIT, Renewable Portfolio Standards (RPS) and the Renewable Energy Certificate (REC) trading have been considered to reduce the government’s expenditure caused by the subsidization. To examine the effectiveness of RPS and REC trading, the development of renewable energy and the environmental and economic benefits under different policies have been quantitatively investigated by using a multi-region power market model and China has been chosen as a case study. The obtained results show that: (i) REC trading can efficiently reduce the government’s expenditure on subsidies for the development of renewable energy; (ii) Compared to FIT, RPS and REC trading will reduce the power sectors’ profit; and (iii) RPS and REC trading may not be enough to achieve the target on renewable energy especially when the capital cost is high, therefore, RPS, REC trade and FIT subsidy should be implemented as complementary policies, not independent.
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Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoTaided SG systems.
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Fluctuated penetration of electric vehicle (EV) loads and production capacities from distributed energy resource (DER) bring large impacts to power systems. To smooth fluctuations, financial incentives have to be maximized for customers controlling their consumption patterns. A fair demand response with electric vehicles (F-DREV) is proposed for the cloud based energy management service. Customers with EV, DER, storage and multiple loads form communities and obtain optimal choices (electricity usage and trading) from F-DREV. F-DREV aims to maximize incentives by minimizing global cost for each community within the given time period, and smooth fluctuations. In order to attract customers to actively participate, we propose the fairness as "customers with higher participation level can reduce their individual cost more than those with lower participation level within the same community", which is attainable by customizing trading prices. A binary linear programming model is formulated, and performances are evaluated in experiments.
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Nowadays, due to the increasing price of fossil fuels and its decreasing resources on the one hand and environmental pollutions on the other hand, use of electric vehicles (EVs) has been increased. Charging EVs has imposed new loads on power systems. These new and major loads along with the deregulation of power systems, which introduces new uncertainties to grid, have caused new challenges for the frequency control and stability of power systems. Use of EVs as moving batteries is one of the ways for dealing with this problem. In this method, EV charging is controlled and, when necessary, EV battery is discharged in grid. This concept is so-called vehicle to grid (V2G). V2G concept is employed in this study for the control of a smart deregulated grid frequency. For this purpose, an optimized fuzzy controller is used to control EVs. Using the proposed method, charging or discharging batteries is carried out with respect to grid frequency and battery state of charge (SOC). To investigate the proposed approach, a modified IEEE 39-bus system in the presence of renewable energy resources is assumed. Then, this system is converted into a three area system in order for the frequency analysis. Investigating the performance of the proposed method for the charging of EVs is done in another part of paper. Simulations are carried out in MATLAB/SIMULINK environment and their results illustrate good performance of the proposed method in the frequency control of deregulated system and EV charging.
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The increasing penetration of renewable energy has become a critical issue in recent years. The future power system is foreseen to depend on distributed energy resource (DER) excessively for continuous load support. Yet, DER providers are also facing limited choices in their produced renewable energy. Massive information and complicated cooperation emerging from involvers intensify issues in terms of efficiency, reliability, and scalability. In this paper, a cloud-based framework is proposed to provide a customer-oriented energy management as a service (EMaaS) for green communities, which are formed as virtual retail electricity providers (REPs) by involved DERs providers. It can be adopted by existing REPs or utilities. For each green community, the multiperiod global cost is minimized to promote renewable energy, and renewable energy consumption is stabilized to enhance integration. A solvable linear programming model is formulated for EMaaS. The case studies results reveal the proposed EMaaS retains satisfactory performances.