Abstract

From the availability of resources to the accomplishment of tasks, cloud computing is a development of supercomputing. One of the most trustworthy paradigms in computing technology is built on internet-based parallel and distributed computing models. Optimization algorithms can be used to distribute user workloads to provided logical resources termed 'Virtual Machines' in the cloud computing system, which is a major aspect of resource management (VM). A fundamental challenge in cloud computing is the dynamic heterogeneity of resources and workloads, which necessitates efficient task scheduling and distribution. It is possible that task scheduling in distributed environments will improve our understanding of workflow scheduling, independent task scheduling that takes into account security and execution time for applications, trust between various system entities, and improved system utilisation and energy efficiency, among other things. The goal of this research is to contribute to these advancements in these areas: An independent task scheduling system based on genetics is presented to obtain the best outcomes in terms of time and resource consumption while allocating tasks to resources in accordance with the task's security needs. Various meta-heuristic algorithms, such as Genetic Algorithm, are currently being used to solve task scheduling difficulties.