An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment

Full Text (PDF, 457KB), PP.74-79

Views: 0 Downloads: 0

Author(s)

Shaminder Kaur 1,* Amandeep Verma 1

1. Department of IT, UIET, Panjab University, Chandigarh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2012.10.09

Received: 18 Jan. 2012 / Revised: 3 May 2012 / Accepted: 15 Jul. 2012 / Published: 8 Sep. 2012

Index Terms

Cloudlets, Cloud Computing, Genetic Algorithm, Makespan, Task-Scheduling

Abstract

Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.

Cite This Paper

Shaminder Kaur, Amandeep Verma, "An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.10, pp.74-79, 2012. DOI:10.5815/ijitcs.2012.10.09

Reference

[1]Kaur, P.D., Chana, I. “Unfolding the distributed computing paradigm” ,In: International Conference on Advances in Computer Engineering, pp. 339-342 (2010)

[2]Mei, L., Chan, W.K., Tse, T.H., “A Tale of Clouds: Paradigm Comparisons and Some Thoughts on Research Issues”, In: APSCC 2008, pp. 464-469 (2008)

[3]Silva, J.N., Veiga, L., Ferreira, P.: “Heuristics for Resource Allocation on Utility Computating Infrastructures. In: 6th International Workshop on Middleware for Grid Computing, New York (2008)

[4]Mell, P., Grance, T., “The NIST Definition of Cloud Computing”, Version 15, 10-7-09. National Institute of Standard and Technology, Information technology Laboratory (2009)

[5]Salesforce Customer Relationship Management (CRM) system, http://www.salesforce.com/in/?ir=1

[6]http://code.google.com/appengine

[7]Dikaiakos, M., katsaros, D., Mehra, P., Vakali, A.: “Cloud Computing: Distributed Internet Computing for IT and Scientific Research”. In:IEEE Transactions on Internet Computing 13(5), pp. 10-13 (2009)

[8]Sadhasivam, S.,Nagaveni, N.: “Design and Implementation of an efficient Two-level Scheduler for Cloud Computing Environment”. In: International Conference on Advances in Recent Technologies in Communication and Computing, pp. 884-886 (IEEE 2009)

[9]Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: “Cost Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads. In: 3rd IEEE International Conference on Cloud Computing, Miami (July 2010) 

[10]Tayal, S.: “Tasks Scheduling Optimization for the Cloud Computing Systems”. In: (IJAEST) International Journal of Advanced Engineering Sciences and Technologies, vol. 5, Issue No.2, pp. 111-115 (2011)

[11]Ge, Y., Wei, G.: “GA-Based Task Schedular for the Cloud Computing Systems”. In: IEEE International Conference on Web Information Systems and Mining, pp 181-186, WISM.2010.87 (2010)

[12]Zhao, L., Ren, Y., Sakurai, K.: “A Resource Minimizing Scheduling Algorithm with Ensuring the Deadline and Reliability in Heterogeneous Systems”. In: International Conference on Advance Information Networking and Applications, AINA.( IEEE 2011)

[13]Sindhu, S., Mukherjee S.: “Efficient Task Scheduling Algorithms for Cloud Computing Environment”. In: International Conference on High Performance Architecture and Grid Computing (HPAGC-2011), vol 169, pp 79-83 (2011)