A Comparative Analysis of Energy-Efficient and Improved QoS-Driven Task and Resource Scheduling in Mobile Cloud Computing Environment

Abstract

Mobile Cloud Computing (MCC) is a combination of cloud computing in to a mobile environment. It is refers to an infrastructure where data storage and data processing happen outside of the mobile device. MCC is an computing platform located in clouds, which is accessed over the wireless connection. MCC can significantly enhance the computation capability and saves energy of the smart mobile devices. Some built in defects of mobile devices, such as limited battery energy, insufficient storage; the mobile applications faces many challenges in mobility management, Quality of Service (QoS), energy management and security issues. A task is an application which is running in a mobile device and those tasks will be executed by Virtual Machines (VM) which is known as Resources. The pool of VM in a cloud computing data center needs to manage an efficient task and resource scheduling to maintain efficient energy, QoS and resource utilization. This work investigates comparative analysis of energy efficient and improved QoS-driven Task and Resource scheduling in a MCC environment by using Differential Evolution (DF). The evaluation of these algorithms is based on energy and QoS metrics. Based on the analysis of the simulation result, one of the scheduling will be concluded as best scheduling process in terms of energy and QoS.