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  • Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy - Accepted

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Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy

Research output: Contribution to journalJournal article

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<mark>Journal publication date</mark>1/06/2018
<mark>Journal</mark>IEEE Transactions on Parallel and Distributed Systems
Issue number6
Volume29
Number of pages15
Pages (from-to)1317-1331
Publication statusPublished
Early online date28/03/17
Original languageEnglish

Abstract

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.

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©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.