Open Access

ARTICLE

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Muhammad Imran Khan Khalil¹, Syed Adeel Ali Shah¹, Izaz Ahmad Khan², Mohammad Hijji³, Muhammad Shiraz⁴, Qaisar Shaheen^5,*

1 Department of Computer Science & Information Technology, University of Engineering & Technology, Peshawar, 25000, Pakistan
2 Department of Computer Science, Bacha Khan University, Charsadda, 24541, Pakistan
3 Computer Science Department, University of Tabuk, Tabuk, 47512, Saudi Arabia
4 Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, 44000, Pakistan
5 Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Rahim Yar Khan Campus, Punjab, Pakistan

* Corresponding Author: Qaisar Shaheen. Email:

Computers, Materials & Continua 2023, 75(3), 6305-6322. https://doi.org/10.32604/cmc.2023.038163

Received 29 November 2022; Accepted 22 February 2023; Issue published 29 April 2023

Abstract

Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers string-matching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC. The primary goal of this study is to use a string-matching algorithm (i.e., Boyer Moore) to compare the contents of incoming workloads to those of documents that have already been processed in a data center. A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy. On the contrary, if no match can be discovered, the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices, the number of active servers, on-site green energy, and traces of incoming workload. The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

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Keywords

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