Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim

Abstract

As the number of cloud users are spontaneously growing globally, there is an urgent need to constantly provide quality services to consumers. Consequently, task scheduling plays an essential role in improving the performance of the cloud computing environment. Most of the published research in this field share common goals, which can be summarized in maximizing resource utilization, reducing cost, and increasing performance. This research provides the foundation knowledge on the latest works done to enhance and optimize the existing task scheduling algorithm in cloud computing by considering various parameters. Furthermore, in this study, we have applied comparative study to analyze the performance of three task scheduling algorithms namely Max-Min, First Come First Serve (FCFS), and Round Robin (RR) in cloud computing environments based on the performance metric of the Virtual Machines (VM) resources' cost, average time and makespan to find the best performing algorithm in the cloud environment. The experimental evaluations were conducted using CloudSim simulation tool. The results show that Max-Min achieved better performance based on makespan and average waiting time than other algorithms in Space and Time-shared policies.

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

“…The value of Random Forest RMSE is the lowest compared to the other models and a good value for it is usually between 0.5 and 0.2. While the MSE Random Forest has the lowest value and when even the value is closer to zero is considered to perform better [36,37]. Moreover, the R-squared Random Forest gives the highest value and whenever the value is closer to 1 is better.…”