Open Access

ARTICLE

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

R. Rengaraj alias Muralidharan^1,*, K. Latha²

1 Department of Information Technology, Saranathan College of Engineering, Tiruchirapalli, 620012, Tamilnadu, India
2 Department of Computer Science and Engineering, Anna University (BIT Campus), Trichy, 620024, Tamilnadu, India

* Corresponding Author: R. Rengaraj alias Muralidharan. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1923-1937. https://doi.org/10.32604/iasc.2023.029495

Received 04 March 2022; Accepted 12 April 2022; Issue published 19 July 2022

Abstract

In cloud computing (CC), resources are allocated and offered to the clients transparently in an on-demand way. Failures can happen in CC environment and the cloud resources are adaptable to fluctuations in the performance delivery. Task execution failure becomes common in the CC environment. Therefore, fault-tolerant scheduling techniques in CC environment are essential for handling performance differences, resource fluxes, and failures. Recently, several intelligent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics. With this motivation, this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme (GTO-FTASS) in CC environment. The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account. The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops. Besides, the GTO-FTASS model derives a fitness function involving two parameters such as expected time of completion (ETC) and failure probability of executing a task. In addition, the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model. The experimental validation of the GTO-FTASS model has been performed and the results are inspected under several aspects. Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

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Keywords

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