Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

Abbasi-khazaei, Tahereh; Rezvani, Mohammad Hossein

doi:10.21203/rs.3.rs-1233833/v1

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The findings demonstrate that the suggested algorithm achieves the optimal solution with improved precision and computational speed. In [8], carbon-efficient virtual machine placement was addressed. Solar energy was also explored as a source of energy supply to lower energy and carbon consumption expenses.…”

Section: Introduction a Background And Literature Reviewmentioning

confidence: 99%

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

2023

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Renewable energy sources (RESs) are becoming promising nowadays. Deploying distributed energy resources (DERs) is thus increasing accordingly, but the intermittent nature associated with RES remains a significant challenge. Also, the distribution grid faces technological difficulties with the largescale integration of PV generations and EV charging loads. This paper suggests a strategy for a novel optimal voltage regulator (VR) placement model associated with the optimal placement of electric vehicle chargers (EVCs) and photovoltaic (PV) cells. Also, VRs' reference voltages are determined optimally to enhance the hosting capacity (HC) of three-phase unbalanced distribution feeders for large-scale residential/public EVCs and PVs. Two scenarios are defined to study the impact of VRs on the network's HC for DERs, namely with and without VRs in the real three-phase unbalanced feeder. The developed model determines the optimal placement of VRs and their reference voltages linked with EVCs and PVs penetration. Time-Varying Acceleration Coefficients Iteration Particle Swarm Optimization (TVAC-IPSO) algorithm has been implemented to improve the real three-phase unbalanced distribution feeder's HC for DERs. The efficiency of the proposed model and the solution method is validated on the modified IEEE 37-node medium voltage (MV) radial distribution network connected to 35 low voltage (LV) distribution feeders. The obtained results represent that installing a proper number of VRs will increase the network's HC for DERs. INDEX TERMS Electric vehicle (EV), Particle swarm optimization (PSO), Photovoltaic (PV), Unbalanced distribution network, Voltage regulator (VR) NOMENCLATURE 1 This article has been accepted for publication in IEEE Access.

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Section: Introduction a Background And Literature Reviewmentioning

confidence: 99%

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

2023

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“…The other group [7], [8] focuses on developing sophisticated strategies for task placement. Data analysis consists of multiple data-dependency tasks, whose processing time or energy cost [53] can be shortened by appropriately allocating the tasks. Different task placement strategies lead to different results.…”

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confidence: 99%

JHTD: An Efficient Joint Scheduling Framework Based on Hypergraph for Task Placement and Data Transfer Across Geographically Distributed Data Centers

Jing

Dan

2022

IEEE Access

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As the explosive growth of the data volume, data center is playing a critical role to store and process huge amount of data. Traditional single data center can no longer to adapt into incredibly fastgrowing data. Recently, some researches have extended the tasks such data processing to geographically distributed data centers. However, since the joint consideration of task placement and data transfer, it is complex and difficult to design a proper scheduling approach with the goal of minimizing makespan under the constraint of task dependencies, processing capability and network, etc. Therefore, our work proposes JHT D : an efficient joint scheduling framework based on hypergraph for task placement and data transfer across geographically distributed data centers. Generally, there are two crucial stages in JHT D. Initially, due to the outstanding of hypergraphs in modeling complex problems, we have leveraged a hypergraph-based model to establish the relationship between tasks, data files, and data centers. Thereafter, a hypergraph-based partition method has been developed for task placement within the first stage. In the second stage, a task reallocation scheme has been devised in terms of each task-to-data dependency. Meanwhile, a data dependency aware transferring scheme has been designed to minimize the makespan. Last, the real-world model China-VO project has been used to conduct a variety of simulation experiments. The results have demonstrated that JHT D effectively optimizes the problems of task placement and data transfer across geographically distributed data centers. JHT D has been compared with three other stateof-the-art algorithms. The results have demonstrated that JHT D can reduce the makespan by up to 20.6%. Also, various impacts (data transfer volume and load balancing) have been taken into account to show and discuss the effectiveness of JHT D.INDEX TERMS Big data processing, Geographically distributed data centers, Joint scheduling framework, Hypergraph, Task placement, Data transferring. I. INTRODUCTIONWith the advent of Big Data era, the rate of data generation is dramatically increasing. For example, Internet giants such as Google and Facebook crunch more than 10 PB of data a day [1]. As a result, it is essential to improve the efficiency of data processing in the face the huge amount of data.MapReduce [2] and Spark [3] have been widely adopted to deal with large amounts of data. These frameworks usually process data analytic jobs characterized by data-dependency awareness. These jobs can be divided into a set of dependent tasks. The execution of a task not only requires the outcome of the parent tasks, but also the data. Normally, the data and

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Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

Cited by 2 publications

References 26 publications

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

JHTD: An Efficient Joint Scheduling Framework Based on Hypergraph for Task Placement and Data Transfer Across Geographically Distributed Data Centers

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