Abstract:

Cloud computing almost trending technologies in now-a-day, which enables users (including individuals and businesses) to remotely access computer resources (software, hardware, and platform) as services over the Internet. Scalability, changeable costs, accessibility, dependability, and on-demand pay-as-you-go services set cloud computing apart from conventional computing paradigms. Cloud computing must be able to answer all user demands with high performance and a guaranty of service quality because it serves millions of users concurrently (QoS). Hence, in order to fairly and effectively fulfill these requirements, we must build a suitable job scheduling system. One of the most important problems in the cloud computing environment is task scheduling because it directly affects how well the cloud performs.There are many different kinds of scheduling algorithms, including dynamic scheduling algorithms that are thought to be appropriate for large-scale cloud computing environments and static scheduling methods that are thought to be ideal for small or medium-sized cloud computing. In this study, we aim to demonstrate the performance of the three most widely used static task scheduling algorithms: first come first served (FCFS), short job first scheduling (SJF), and MAX-MIN. Their effects on algorithm complexity, resource availability, total execution time (TET), total waiting time (TWT), and total finish time(TFT) have been evaluated using the CloudSim simulator.

Description:

task scheduling algorithms, load balance, performance