Investigating different optimization criteria for a hybrid job scheduling approach based on heuristics and metaheuristics

The Information Technology industry has revolutionized through the advent of cloud computing as the cloud offers dynamic computing utilities to global users. The performance of cloud computing services depends on the process of job scheduling. There has been a great research focus on the different amalgamation of heuristics with meta-heuristics (hybrid scheduling approaches) in the cloud computing scheduling context with the aim of optimizing several performance metrics. This paper discusses a hybrid job scheduling approach that intends to optimize the performance metrics namely makespan, average flow time, average waiting time, and throughput. The main focus of this paper is to evaluate this hybrid job scheduling approach based on different optimization criteria which includes single-objective and multi-objectives functions based on the aforementioned performance metrics on different large-scale problem instances. This helps us to investigate and identify the best optimization criteria for the hybrid job scheduling approach

A Hybrid Job Scheduling Approach on Cloud Computing Environments on the Usage of Heuristics and Metaheuristics Methods

The Information Technology Industry has been revolutionized through Cloud Computing by offering dynamic computing services to users through its on-demand provisioning of scalable and virtualized resources over the internet on a pay-per-use measured basis. Performance improvements in task scheduling can have a great impact on the efficiency of cloud computing. This paper proposes a hybrid task scheduling approach which employs the metaheuristic optimization technique, genetic algorithm to produce a certain combination of scheduling heuristics for processing cloud workloads. This approach is developed to optimize the performance metrics namely makespan, average flow time, throughput, and average waiting time. The developed approach is evaluated on the CloudSimPlus simulation framework using large-scale benchmarks against other heuristics in terms of the stated performance metrics. The results indicate that the proposed hybrid approach consistently outperforms the baseline individual heuristics in terms of the stated metrics irrespective of the scale of the workload. It is also observed that the optimization potential tends to increase as the workload scale becomes heavier and optimizing flow time produces complementary effects on the other metrics