Empirical Analysis of Resource Scheduling Algorithms in Cloud Simulated Environment

The cloud environment is a collection of resources providing multiple services to the end-users. Users submit tasks to this cloud computing environ ment for computation purposes. Using statically fixed resource scheduling algo rithms, the cloud accepts these tasks for computations on its Virtual Machines (VM). Resource scheduling is considered a complex job, and computing these challenging tasks without external intelligence becomes a challenge for the cloud. The key objective of this study is to compute different task sizes in the Work flowSim cloud simulation environment using scheduling algorithms Max – Min (MxMn), Minimum Completion Time (M.C.T.), and Min – Min (MnMn) and later compare their behavior concerning various performance metrics. The exper imental results show that all these algorithms have dissimilar behavior, and no method supplies the best results under all performance metrics. Therefore, an in telligence mechanism is required to be provided to these resource scheduling al gorithms so they can perform better for all the performance metrics. Lastly, it is suggested that Reinforcement Learning (RL) acts as an intelligence mechanism, enhances the resource scheduling procedure, and makes the entire process dynamic, enhancing cloud performance.