QoS-Aware Efficient Tasks Scheduling in Heterogeneous Cloud Computing Environment

Abstract

Quality of Service (QoS) is a set of technologies and techniques that are used to manage and prioritize network traffic to ensure that important data is transmitted efficiently and effectively. It helps to ensure that critical applications and services receive the necessary bandwidth and resources to function properly, while less important traffic is given a lower priority. This is accomplished by classifying and marking packets and then further process on it. Task scheduling is the process of arranging, coordinating, and managing the execution of tasks, typically in a computer system or network. This can include the allocation of resources such as CPU time, memory, and storage, as well as the order in which tasks are executed. Task scheduling algorithms are used to determine the most efficient way to execute a set of tasks, based on factors such as priority, resource requirements, and dependencies between tasks. A heterogeneous cloud environment refers to a scenario where multiple cloud computing platforms from different vendors are used in combination to deliver a specific set of services. This can include using a mix of public, private, and hybrid clouds, as well as different types of infrastructure such as IaaS, PaaS, and SaaS. The goal of a heterogeneous cloud environment is typically to leverage the strengths of each platform in order to optimize cost, performance, and scalability. In cloud computing, the task scheduling problem necessitates the effective mapping of workloads to virtual resources. The genetic-based algorithm BGA will be introduced in a study in order to increase the makespan and its resource consumption. On the other hand, the SGA is concerned with convergence speed. The strategies given are compared to several current meta-heuristic and heuristic techniques.