A load-aware resource allocation and task scheduling for the emerging cloudlet system

Abstract

Cloudlet-assisted mobile cloud computing (MCC) emerges as a vital paradigm to address the problems of mobile services such as application time-out, data caching and traffic congestion in wireless network. The cloudlet has adequate resources to process multiple mobile requests simultaneously, but it is not as sufficient as a remote cloud data center. Currently the performance of MCC system is a subject to the lengthy network transmission latency due to the long distance between cloudlet and remote cloud. In this article, we focus on the variable user’s QoS requirements and budget of cloudlet provider, design a load-aware resource allocation and task scheduling (LA-RATS) strategy which adaptively allocates resource in MCC system for delay-tolerant and delay-sensitive mobile applications according to cloudlet’s load profile. Subsequently, a tree generation based task backfilling algorithm is proposed to raise the utilization of the cloudlet. Particularly, when cloudlet is overloaded, the restrictions of delay-sensitive applications’ deadlines are satisfied through further offloading the allocated delay-tolerant tasks in the cloudlet to distant cloud. From several systematic evaluations, it is shown that our strategy can significantly reduce the cloudlet’s monetary cost and turnaround time for delay-tolerant applications, and increase the deadline satisfaction rate of delay-sensitive applications.