Open Access

ARTICLE

Hybrid Chameleon and Honey Badger Optimization Algorithm for QoS-Based Cloud Service Composition Problem

G. Manimala^*, A. Chinnasamy

Department of Computer Science & Engineering, Sri Sai Ram Engineering College, Chennai, Tamil Nadu, India

* Corresponding Author: G. Manimala. Email:

Computer Systems Science and Engineering 2023, 47(1), 393-412. https://doi.org/10.32604/csse.2023.037066

Received 21 October 2022; Accepted 09 February 2023; Issue published 26 May 2023

Abstract

Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service (SaaS). Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources. In this context, the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests, as the services stored over the cloud are too complex and scalable. To achieve better service composition, the parameters of Quality of Service (QoS) related to each service considered to be the best resource need to be selected and optimized for attaining potential services over the cloud. Thus, the cloud service composition needs to concentrate on the selection and integration of services over the cloud to satisfy the client’s requests. In this paper, a Hybrid Chameleon and Honey Badger Optimization Algorithm (HCHBOA)-based cloud service composition scheme is presented for achieving efficient services with satisfying the requirements of QoS over the cloud. This proposed HCHBOA integrated the merits of the Chameleon Search Algorithm (CSA) and Honey Badger Optimization Algorithm (HBOA) for balancing the trade-off between the rate of exploration and exploitation. It specifically used HBOA for tuning the parameters of CSA automatically so that CSA could adapt its performance depending on its incorporated tuning factors. The experimental results of the proposed HCHBOA with experimental datasets exhibited its predominance by improving the response time by 21.38%, availability by 20.93% and reliability by 19.31% with a minimized execution time of 23.18%, compared to the baseline cloud service composition schemes used for investigation.

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Keywords

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