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A Task-Based Greedy Scheduling Algorithm for Minimizing Energy of MapReduce Jobs
, Article Journal of Grid Computing ; Volume 16, Issue 4 , 2018 , Pages 535-551 ; 15707873 (ISSN) ; Goudarzi, M ; Sharif University of Technology
Springer Netherlands
2018
Abstract
MapReduce and its open source implementation, Hadoop, have gained widespread adoption for parallel processing of big data jobs. Since the number of such big data jobs is also rapidly rising, reducing their energy consumption is increasingly more important to reduce environmental impact as well as operational costs. Prior work by Mashayekhy et al. (IEEE Trans. Parallel Distributed Syst. 26, 2720–2733, 2016), has tackled the problem of energy-aware scheduling of a single MapReduce job but we provide a far more efficient heuristic in this paper. We first model the problem as an Integer Linear Program to find the optimal solution using ILP solvers. Then we present a task-based greedy scheduling...
Power-aware performance analysis of self-adaptive resource management in IaaS clouds
, Article Future Generation Computer Systems ; Volume 86 , 2018 , Pages 134-144 ; 0167739X (ISSN) ; Entezari Maleki, R ; Etesami, S. E ; Egger, B ; Ardagna, D ; Movaghar, A ; Sharif University of Technology
Elsevier B.V
2018
Abstract
In this paper, Stochastic Activity Networks (SANs) are used to model and evaluate the performance and power consumption of an Infrastructure-as-a-Service (IaaS) cloud. The proposed SAN model is scalable and flexible, yet encompasses some details of an IaaS cloud, such as Virtual Machine (VM) provisioning, VM multiplexing, and failure/repair behavior of VMs. Using the proposed SAN, a power-aware self-adaptive resource management scheme is presented for IaaS clouds that automatically adjusts the number of powered-on Physical Machines (PMs) regarding variable workloads in different time intervals. The proposed scheme respects user-oriented metrics by avoiding Service Level Agreement (SLA)...