A Fault Tolerant Hybrid Genetic Algorithm for Task Scheduling in Cloud Computing Environment

Abstract

Task scheduling is the most critical issue in the cloud computing environment as the performance of the cloud depends primarily on it. Task scheduling algorithms are defined as a set of rules and policies used to assign tasks to suitable resources like memory, CPU, and bandwidth to get the highest level possible of performance and resource utilization. There are different types of scheduling algorithms which are broadly categorized into static and dynamic algorithms. The static algorithms are considered suitable for small or medium-scale cloud computing and the dynamic scheduling algorithms are suitable for large-scale cloud computing environments. The research work conducted primarily focused on three prominent task scheduling algorithms: FCFS (First Come First Serve), RR (Round Robin), and SJF (Shortest Job First). The performance of these three task scheduling algorithms has been analyzed in both virtualandnon-virtual environments. The performance has been evaluated using makespan, utilization rate, average waiting time, and average turnaround time as performance evaluation parameters with varying processes and virtual machines. A swarm can be defined as a structured collection of interacting organisms (or agents). Swarm intelligence algorithms are a form of nature-based optimization algorithms. The individuals within a swarm interact to solve a global objective in a more efficient manner than one single individual could. Their chief motivation is the supportive behavior of animals within explicit communities. This can be labeled as simple behaviors of individuals along with the mechanisms for sharing knowledge between them, resulting in the multifaceted behavior of the whole community. Swarm Intelligence (SI) is the property of a system whereby the cooperative behaviors of (unsophisticated) agents intermingling locally with their environment cause coherent functional global patterns to emerge. The basis of SI is to explore collective (or distributed) problem solving without centralized control or the provision of a global model. Examples of such behavior can be found in bird flocks, ant colonies, fireflies, bee swarms, or grey wolves. Several swarm optimization algorithms have been brought forward since the early 60s. All of these algorithms have validated their potential to answer numerous optimization glitches This research offers an in-depth survey of recognized optimization algorithms. newline