Task scheduling in cloud computing environment using hybrid genetic algorithm and artificial bee colony

The fundamental issue with cloud computing is task scheduling and decreasing system performance. An efficient task-scheduling technique is essential to increase system performance. Existing task-scheduling algorithms are primarily concerned with task resource requirements, CPU memory, execution time, and cost. These, on the other hand, do not examine network bandwidth. In cloud computing systems, task scheduling is essential. Task scheduling cannot be done based on a single criterion but rather under a set of rules and regulations that we might refer to as a cloud user-provider agreement. This agreement is more than the user’s expectations about the providers’ service quality. Providing high-quality services to users following the consensus is a crucial duty for providers, juggling a vast number of other responsibilities. The task scheduling problem can be thought of as discovering or discovering an optimal mapping/assignment of a series of subtasks of various tasks over a set of available resources (processors/computer machines) to fulfil the intended task goals. During the methodology chapter, a comprehensive investigation has been done to ascertain the proposed method that can be adopted such as algorithms involved, project flow, and simulation. This is essential to produce a system that has a feature such as web-based system that is able to generate a report from the simulation. In this project, a comparative evaluation of selected algorithms is done to ascertain their applicability, practicality, and adaptability in a cloud scenario. At the end of the project, the author will attempt to suggest an algorithm that can be utilized to expand the present platform further. As a result, cloud providers will be able to provide higher-quality services