Dynamic Virtual Machine Scheduling for Resource Sharing In the Cloud Environment

ABSTRACT: Resource allocation and job scheduling are the core functions of cloud computing. These functions are based on adequate information of available resources. Timely acquiring dynamic resource status information is of great importance in ensuring overall performance of cloud computing. A cloud system for analyzing performance, removing bottleneck, detecting fault, and maintaining dynamic load balancing, thus, to help cloud users obtain desired computing results by efficiently utilizing system resources in terms of minimized cost, maximized performance between cost and performance. To reduce overhead, the aim of designing a dynamic resource allocation and prediction system is to achieve seamless fusion between cloud technologies and efficient resource scheduling and prediction strategies. This work aims at building a distributed system for cloud resource scheduling and prediction. In this project, we present the design and evaluation of system architecture for cloud resource scheduling and prediction. We discuss the key effects for system implementation, including virtual machine learning-based methodologies for modeling and optimization of resource prediction models. Evaluations are executed on a prototype system. Our experimental results suggest that the efficiency and accuracy of our system meet the demand of online system for dynamic resource utilization and prediction.