Analyzing Heuristic Job Scheduling Algorithms by Varying Cloudlet Load in a Cloud Infrastructure

• Published in: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) pp. 531 - 535
• Main Authors: Jain, Vivek, Chouhan, Shivani, Goyal, K. K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.12.2021
• Subjects: Cloud Computing; Heuristic algorithms; Job Scheduling; Makespan; Scheduling; Scheduling algorithms; Servers; Task analysis; Time factors
• ISBN: 9781665439688; 1665439688
• ISSN: 2767-7362
• DOI: 10.1109/SMART52563.2021.9676267

The cloud based innovative applications are increasing regularly and hence the data and job load also increasing proportionally. Cloud based service providers are also increasing their infrastructure and service facility to serve in a much better way to its clients. The job processing load will also increase the waiting time and hence affect the service response time at user's end. So, it is always a matter of great importance that which job scheduling algorithm should be applied to serve the client in an efficient manner. This is the main motivation for framing this research paper. In this paper, we are taking the main five heuristic job scheduling algorithms like FCFS (First Come First Server), SJF (Shortest Job First), MaxMin, MinMin, and Saffrage for analyzing on the pre-decided cloud infrastructure. Among these heuristic algorithm, MaxMin algorithm outperforms than others in all the test cases i.e. with the cloudlet load of 100, 200, 300, ..., 1000 cloudlets. Hence we can say that the MaxMin is the best scheduling algorithm among these five heuristic job scheduling algorithms.