An Adaptive Framework for Improving Quality of Service in Industrial Systems

Abstract

Limited memory bandwidth is considered as the major bottleneck in multimedia cloud computing for more and more virtual machines (VMs) of multimedia processing requiring high memory bandwidth simultaneously. Moreover, contending memory bandwidth among parallel running VMs leads to poor quality of service (QoS) of the multimedia applications, missing the deadlines of these soft real-time multimedia applications. In this paper, we present an adaptive framework, Service Maximization Optimization (SMO), which is designed to improve the QoS of the soft real-time multimedia applications in multimedia cloud computing. The framework consists of an automatic detection mechanism and an adaptive memory bandwidth control mechanism. With the automatic detection mechanism, the critical section to the multimedia application performance in the VMs is detected. Then, our adaptive memory bandwidth control mechanism adjusts the memory access rates of all the parallel running VMs to protect the QoS of the soft real-time multimedia applications. From the case studies with real-world multimedia applications, our SMO significantly improves the QoS of the soft real-time multimedia applications with a negligible penalty on system throughput.