Comparative study of Multi Objective Task Scheduling using Metaheuristics in Multi-Cluster Environment

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Published: Jun 28, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i5.3912
Keywords:
Task Sequencing, multiple clusters, multi-objective optimization, scheduling, meta-heuristics, multi cluster allocation

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Vishal Mehra
Guru Nanak Dev University
Amit Chhabra
GURU NANAK DEV UNIVERSITY

Abstract

This paper emphasises the study of various algorithms inspired by the nature in order to search for the optimal solutions when multiple criterions are to be considered. There are certain situations witnessed every time when a perfect solution is hard to be obtained the problems called NP Problems. In such situations no single solution or set of solutions as in case of multi-objective optimization can be considered as final result. Thus it is required to consider the solution/set of solutions those best fulfil our requirements. The author puts emphasis on study of Flower Pollination Algorithm for scheduling purpose only. The allocation of processors is based on non deterministic manner as per the availability of processors.

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Vol. 8 No. 5 (2017): May-June 2017
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References

Yang, Xin-She, Mehmet Karamanoglu, and Xingshi He. "Flower pollination algorithm: a novel approach for multiobjective optimization." Engineering Optimization 46.9 (2014): 1222-1237.

Hosseini, Hamed Shah. "Problem solving by intelligent water drops." Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. IEEE, 2007.

Xibo Jin, Fa Zhangb, Liya Fane, Ying Songc, Zhiyong Liu, “Fine Scheduling for energy minimization on restricted parallel processors, Journal of Parallel and Distributed Computing.

Sapinderjeet Kaur, Amit Chhabra,"Survey of Various Meta-Heuristic Algorithms for Parallel Job Scheduling ".

Kuo-Chan Huang, Kuan-Po Li, "Processor Allocation Policies for Reducing Resource Fragmentation in Multi-Cluster Grid and Cloud Environments " International Computer Symposium(ICS2010),2010.

Maziar Yazdani, Fariborz Jolai, “Lion Optimization Algorithm: A Nature Inpired Meta Heuristic Algorithm,†Journal. ofComputationalDesignandEngineering3(2016)24–36].

Tian-Qi Wu, Min Yao,Jian-Hua Yang, "Dolphin Swarm Algorithm" Wu et al. / Front Inform Technol Electron Eng 2016 17(8):717-729.

Suash Deb, Simon Fong, Zhonghuan Tian, “Elephant Search Algorithm for Optimization Problem†The Tenth International Conference on Digital Information Management (ICDIM 2015).

Gabaldan, Eloi, et al. "Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments." The Journal of Supercomputing (2016).

Gabaldon, Eloi, et al. "Particle Swarm Optimization Scheduling for Energy Saving in Cluster Computing Heterogeneous Environments." Future Internet of Things and Cloud Workshops (FiCloudW), IEEE International Conference on. IEEE, 2016.

Blanco, Héctor, et al. "Multiple job co-allocation strategy for heterogeneous multi-cluster systems based on linear programming." The Journal of Supercomputing 58.3 (2011): 394-402.

Rahmani, Amir Masoud, and Mojtaba Rezvani. "A novel genetic algorithm for static task scheduling in distributed systems." International Journal of Computer Theory and Engineering 1.1 (2009):