Energy-Aware Task Consolidation Technique for Cloud Computing

Hsu, Ching‐Hsien; Chen, Shih-Chang; Lee, Chih-Chun; Chang, Hsi-Ya; Lai, Kuan‐Chou; Li, Kuan‐Ching; Rong, Chunming

doi:10.1109/cloudcom.2011.25

Cited by 49 publications

(26 citation statements)

References 16 publications

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“…The paper [3] proposed an improved ACO algorithm to reduce the execution time and balance the nodes with considering the node's computing ability, network bandwidth and other factors. The paper [4] research the combination model about task and energy, so as to optimize the cloud platform to reduce the power consumption and improve the utilization rate of resources. The paper [5] proposes a dynamic resource allocation strategy, which considers the start time and destruction time of the task, and predicts its calculation ability.…”

Section: Introductionmentioning

confidence: 99%

ACO-HS: A Hybrid Cloud Resource Scheduling Model

Mai¹,

Cheng²

2017

dtetr

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Section: Introductionmentioning

confidence: 99%

ACO-HS: A Hybrid Cloud Resource Scheduling Model

Mai¹,

Cheng²

2017

dtetr

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“…In the present paper, the task consolidation problem has been addressed to assign n tasks to a set of different resources and the utilization of nodes and distributed VMs are maintained by energy efficiency and load management. The availability of computer nodes during the power consumed by the cloud is the prime concern of the developed algorithm [9][10].…”

Section: Introductionmentioning

confidence: 99%

Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

Gourisaria

Patra

Khilar

2016

IJECE

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Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy. Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption. Keyword: CPU utilization Energy consumption Idle VM Task consolidation Virtualization VMs Copyright © 2016 Institute of Advanced Engineering and Science.All rights reserved.Corresponding Author:S.S. Patra, School of Computer Application, KIIT University, Bhubaneswar, Odisha, India. Email: sudhanshupatra@gmail.com INTRODUCTIONData communications plays a crucial element of our daily lives and also in the computation. Most of our practices depends on gathering information through the client-server and distributed paradigm [1]. Now a days, client demands have tremendously increased in terms of the number of requests. To cater to the consistent amount of requests, computational capacities and facilities must be constantly reviewed and improved. To remain competitive, the proportional nonnegligible amount of the required energy has been often left behind.Recent advocacy and tightly coupled with energy consumption called green or sustainable computing has received considerable attention in the field of computation as there is a huge need of minimizing the energy consumption. The scope of sustainable computing goes beyond the main computing

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“…In cloud computing, there is a relationship between energy consumption and resource utilization. Task consolidation is an effective technique greatly enabled by virtualization technologies, which facilitate the concurrent execution of several tasks, maximize resource utilization and in turn reduce the energy consumption [1].…”

Section: Introductionmentioning

confidence: 99%