Affiliation:
1. École de technologie supérieure, Quebec, Canada
2. Thapar Institute of Engineering & Technology, Patiala, Punjab, India
Abstract
Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.
Funder
Tata Consultancy Services (TCS), India
Natural Sciences and Engineering Research Council of Canada
Fonds de recherche du Quebec—Nature et technologies (FRQNT) through PBEEE
Tier 2 Canada Research Chair on the Next Generations of Wireless IoT Networks
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Reference41 articles.
1. Emerson Network Power. [n.d.]. Energy logic: Reducing data center energy consumption by creating savings that cascade across systems. Emerson Network Power. A White Paper from the Experts in Business-Critical Continuity. Emerson Network Power. [n.d.]. Energy logic: Reducing data center energy consumption by creating savings that cascade across systems. Emerson Network Power. A White Paper from the Experts in Business-Critical Continuity.
2. Carnegie Mellon University. [n.d.]. OpenCloud Hadoop cluster trace: Format and schema. Retrieved from http://ftp.pdl.cmu.edu/pub/datasets/hla/dataset.html. Carnegie Mellon University. [n.d.]. OpenCloud Hadoop cluster trace: Format and schema. Retrieved from http://ftp.pdl.cmu.edu/pub/datasets/hla/dataset.html.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献