Open Access

REVIEW

Cloud Datacenter Selection Using Service Broker Policies: A Survey

by Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Ahmad Al-Daraiseh³, Mohammad Bany Taha⁴, Mohammad Aladaileh²

1 Department of Information Security, Faculty of IT, University of Petra, Amman, 11196, Jordan
2 Department of Cybersecurity, School of IT, American University of Madaba (AUM), Amman, 11821, Jordan
3 Department of Computer Science, School of IT, American University of Madaba (AUM), Amman, 11821, Jordan
4 Department of Data Science and Artificial Intelligence, School of IT, American University of Madaba (AUM), Amman, 11821, Jordan

* Corresponding Author: Yousef Sanjalawe. Email:

Computer Modeling in Engineering & Sciences 2024, 139(1), 1-41. https://doi.org/10.32604/cmes.2023.043627

Received 07 July 2023; Accepted 15 September 2023; Issue published 30 December 2023

Abstract

Amid the landscape of Cloud Computing (CC), the Cloud Datacenter (DC) stands as a conglomerate of physical servers, whose performance can be hindered by bottlenecks within the realm of proliferating CC services. A linchpin in CC’s performance, the Cloud Service Broker (CSB), orchestrates DC selection. Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck, endangering service quality. To tackle this, deploying an efficient CSB policy becomes imperative, optimizing DC selection to meet stringent Quality-of-Service (QoS) demands. Amidst numerous CSB policies, their implementation grapples with challenges like costs and availability. This article undertakes a holistic review of diverse CSB policies, concurrently surveying the predicaments confronted by current policies. The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development. Additionally, it extensively clarifies various DC selection methodologies employed in CC, enriching practitioners and researchers alike. Employing synthetic analysis, the article systematically assesses and compares myriad DC selection techniques. These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs. In summation, this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection, highlighting the imperative role of efficient CSB policies in optimizing CC performance. By emphasizing the significance of these policies and their modeling implications, the article contributes to both the general modeling discourse and its practical applications in the CC domain.

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Keywords

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