Cloud computing is a computer paradigm that delivers IT resources as services, such as platforms, apps, and infrastructure, via the Internet. The cloud Computing offers the infrastructure needed to process and compute any kind of data resource, and it is used to handle massive volumes of data. Large, well-known businesses have moved their processing and storage to cloud computing in recent years. Businesses and organizations may lower their infrastructure costs by utilizing cloud computing. Businesses can test their apps faster, more effectively, and with less maintenance. Cloud computing enables the IT team to adjust resources to fluctuating and changing requirements. Allocating resources in cloud computing is intrinsically difficult since more and more people are using various cloud apps in some infrastructure. The majority of resource allocation solutions now in use focus on performance, which is impacted by the volume of applications from scientific and business domains. This article presents an analysis of meta-heuristic approaches for resource allocation in cloud computing systems. When allocating resources in the cloud, the examined meta-heuristic algorithms can achieve much better performance, lower costs, shorter turnaround times, better resource usage, and increased energy efficiency. This study compares several scheduling algorithms for cloud and grid systems using three well-known metaheuristic approaches: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO).
Sharma et al. (Wed,) studied this question.
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