A Hybrid Optimization Algorithm for Enhanced Web services with QoS measures in Cloud Computing

Abstract

In the environment of Big Data analytics worldwide, cloud web services were deployed across Internet and Intranet domains. Moreover, cloud computing, while possessing significant advantages and experiencing rapid development, faces trust complexities, privacy concerns, and security issues. These challenges necessitate the implementation of Quality of Service (QoS) measures in optimisation techniques for web service selection.

This study focuses on the selection of component services and the use of an efficient algorithm with end-to-end quality measures. However, data diversification and service characteristics may reduce the accuracy of these measures. To address this, a novel QoS-based web service selection algorithm was developed, incorporating both weighted and subjective attributes.

The proposed methodology employs a hybrid optimisation algorithm that integrates randomised attribute searches with the Invasive Weed Optimisation (IWO) algorithm. Furthermore, it calculates QoS measures based on the weighted attributes of web services.

Many researchers have applied nature-inspired concepts to deal with optimisation complexities in Big Data, including the Eagle Perching Algorithm to improve the efficiency of cloud web services. In addition, the evolution of the Bald Eagle Search (BES) algorithm has been utilised as a nature-inspired approach, providing an efficient technique for optimisation problems by imitating the behaviour of bald eagles.

The results demonstrate that the proposed methodology achieves improved performance metrics when compared with existing approaches, confirming its effectiveness in the evaluation of web service optimisation.