QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer's expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster.

Automated summary

Due to the proliferation of cloud services, several service providers now offer functionally identical services with different levels of service quality. Offering a cloud assistance arrangement with optimum QoS estimates becomes a complicated task. This study proposes a novel approach using Genetic Algorithms and the Eagle Strategy algorithm to strike a balance between exploration and exploitation, achieving faster QoS-based Cloud Service Selection.