ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services

Hassan Mahmood Khan, Fang Fang Chua*, Timothy Tzen Vun Yap

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
5 Downloads (Pure)

Abstract

Dynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the Quality of Service (QoS), estimate the required resources, and modify allotted resources depending on workload, serialization, and parallelism due to resources. This article focuses on cloud QoS violation remediation using resource planning and scaling. A Resource Quantified Scaling for QoS Violation (ReSQoV) model is proposed based on the Universal Scalability Law (USL), which provides cloud service capacity for specific workloads and generates a capacity model. ReSQoV considers the system overheads while allocating resources to maintain the agreed QoS. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. The scenarios emulate QoS parameters and their respective resource utilization for ReSQoV compared to policy-based resource allocation. The results show that after USLbased Quantified resource allocation, QoS is regained, and validation of the ReSQoV is performed through the statistical test ANOVA that shows the significant difference before and after implementation.

Original languageEnglish
Article number131
JournalFuture Internet
Volume14
Issue number5
Early online date26 Apr 2022
DOIs
Publication statusPublished - May 2022

Keywords

  • cloud computing
  • QoS
  • resource allocation
  • SaaS
  • scalability
  • USL

ASJC Scopus subject areas

  • Computer Networks and Communications

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