A SURVEY AND ANALYSIS OF METAHEURISTIC BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING ENVIRONMENT - ABDULLAHI, MOHAMMED; AHMAD, BARROON ISMAEEL; A. F. DONFACK KANA (BIMA JOURNAL OF SCIENCE AND TECHNOLOGY (2536-6041), 2020)
Cloud service providers wants to schedule as many user applications as possible on each resource to maximize the utilization of resources, while the consumers wish to have their requests served at minimal cost. Since Cloud Computing is an economic setting, cloud service providers and consumers tries to increase their income and return on investment (ROI) by optimal resource. Efficient scheduling of resources becomes central to meeting Service Level Agreements (SLAs) in delivering effective cloud services. When the resource provisioning is overestimated, it leads to under-utilization of resources and loss of revenue. Due to the practical applications and challenges of executing large scale applications, task scheduling of applications on the large scale have become an emerging research in cloud computing and have attracted significant attention of researchers in recent times. Moreover, various heuristics have been applied to solve task scheduling problems which generate optimal solutions for small size problems. However, the quality of solutions produced by these techniques degrades woefully as the problem size and number of variables to be optimized increases. Also, these heuristic methods do not have provisions and support for meeting various Quality of Service (QoS) requirements like response time, makespan time, reliability, availability, energy consumption, cost, resource utilization. In contrast, many cloud users requires certain QoS satisfaction especially for scientific and business domain applications. In recent times, attempts have been made to address task scheduling problems using metaheuristic algorithms to address this problem. Using metaheuristic algorithms for solving task scheduling problems in cloud have shown promising improvements in achieving efficiency, by reducing the solution search space. This paper provides the analysis of metaheuristic algorithms, which we hope to be of great interest to the upcoming researchers in the field of optimizing cloud service resource provisioning.