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PE-DCA: Penalty Elimination Based Data Center Allocation Technique Using Guided Local Search for IaaS Cloud

Sasmita Parida^{1, 2}, Bibudhendu Pati¹, Suvendu Chandan Nayak², Chhabi Rani Panigrahi¹ and Tien-Hsiung Weng³

1. Department of Computer Science
   Rama Devi Women's University, India
   sasmitamohanty5@gmail.com, patibibudhendu@gmail.com, panigrahichhabi@gmail.com
2. Department of Computer Science and Engineering, Gandhi Institute for Technological Advancement
   BPUT, Bhubaneswar, India
   suvendu2006@gmail.com
3. Science and Information Engr. (CSIE)
   Providence University, Taiwan
   thweng@gm.pu.edu.tw

Abstract

In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.

Key words

Cloud Computing, Data Center, Allocation, Penalty, Meta-heuristic, Guided Local Search

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS210512059P

Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Parida, S., Pati, B., Nayak, S. C., Panigrahi, C. R., Weng, T.: PE-DCA: Penalty Elimination Based Data Center Allocation Technique Using Guided Local Search for IaaS Cloud. Computer Science and Information Systems, Vol. 19, No. 2, 679–707. (2022), https://doi.org/10.2298/CSIS210512059P