An effective Multi-Objective task scheduling algorithm using Min-Max normalization in cloud computing
Authors:
- 1 January 2016
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 812-816
Abstract
Cloud computing has gained enormous popularity both in business and academia due its on demand service over Internet to the customers on pay-as-you-go model. Task scheduling in cloud computing is a well known problem that has been paid enormous attention. This is even more challenging, particularly for multi-cloud environment. Due to its NP-Hardness, many heuristics have been developed recently. In this paper, we also present a task scheduling algorithm which is based on the popular Min-Max normalization technique in data mining. We refer our proposed algorithm as Normalized Multi-Objective Min-Min Max-Min Scheduling (NMOMXS). Through simulation, the algorithm is shown to outperform two well known existing algorithms in terms of makespan and resource utilization.Keywords
This publication has 12 references indexed in Scilit:
- Efficient task scheduling algorithms for heterogeneous multi-cloud environmentThe Journal of Supercomputing, 2015
- A systematic review on cloud computingThe Journal of Supercomputing, 2014
- Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithmComputers & Operations Research, 2013
- Online optimization for scheduling preemptable tasks on IaaS cloud systemsJournal of Parallel and Distributed Computing, 2012
- A Taxonomy and Survey of Cloud Computing Systems2009
- Batch mode scheduling in grid systemsInternational Journal of Web and Grid Services, 2007
- A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systemsParallel Computing, 2005
- The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions2002
- Impact of workload and system parameters on next generation cluster scheduling mechanismsIEEE Transactions on Parallel and Distributed Systems, 2001
- A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing SystemsJournal of Parallel and Distributed Computing, 2001