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Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

Atif Ishaq Khan*, Syed Asad Raza Kazmi, Awais Qasim
Department of Computer Science, Government College University, Lahore, 54000, Pakistan
* Corresponding Author: Atif Ishaq Khan. Email:

Computers, Materials & Continua 2023, 74(1), 1183-1197. https://doi.org/10.32604/cmc.2023.032691

Received 26 May 2022; Accepted 27 June 2022; Issue published 22 September 2022

Abstract

A self-adaptive resource provisioning on demand is a critical factor in cloud computing. The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests. Therefore, a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload. In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy (CARSS) Framework that formally addresses these issues and is more expressive than traditional approaches. The decision making in CARSS is based on more than one factors. The MAPE-K based framework determines the state of the resources based on their current utilization. Timed-Arc Petri Net (TAPN) is used to model system formally and behaviour is expressed in TCTL, while TAPAAL model checker verifies the underline properties of the system.

Keywords

Formal modeling; multi-agent; self-adaptive; cloud computing

Cite This Article

A. I. Khan, S. A. R. Kazmi and A. Qasim, "Formal modeling of self-adaptive resource scheduling in cloud," Computers, Materials & Continua, vol. 74, no.1, pp. 1183–1197, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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