Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing

Kamalpreet Kaur*, Kanwalvir Singh Dhindsa**
* PG Scholar, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.
** Associate Professor, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.
Periodicity:May - July'2015
DOI : https://doi.org/10.26634/jcc.2.3.4791

Abstract

Cloud Computing has revolutionized the Information and Communication Technology (ICT) industry by enabling ondemand provisioning of elastic computing resources on a pay-as-you-go basis. Resource Scheduling is a way of determining schedule on which activities should be performed. Resource scheduling is a complicated task in a Cloud environment because of heterogeneity of the computing resources. To allocate the best resource to a Cloud job is a tedious task and the problem of finding the best resource – job pair according to Cloud consumer application requirements is an optimization problem. The main goal of the Cloud scheduler is to schedule the resources effectively and efficiently. Dispersion, heterogeneity and uncertainty of resources bring challenges to resource allocation, which cannot be satisfied with traditional resource allocation policies in Cloud circumstances. In this research paper, the clustering based cost optimized resource scheduling technique has been proposed. In clustering based resource scheduling, classification of these workloads is done through k-means clustering algorithm by assigning the weights to the different quality attributes. The experimental results gathered through Cloud environment clearly demonstrate that the proposed technique has better performance for cost as compared to the existing resource scheduling technique.

Keywords

Cloud Computing, Resource Scheduling, Quality of Service (QoS), Cost, Clustering.

How to Cite this Article?

Kaur, K., and Dhindsa, K.S. (2015). Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing. i-manager’s Journal on Cloud Computing, 2(3), 8-18. https://doi.org/10.26634/jcc.2.3.4791

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