Abstract
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.
Recommended Citation
Remesh, Abhijith; Nahhas, Abdulrahman; Kharitonov, Andrey; and Turowski, Klaus, "Investigating different optimization criteria for a hybrid job scheduling approach based on heuristics and metaheuristics" (2022). ACIS 2022 Proceedings. 48.
https://aisel.aisnet.org/acis2022/48