QoS-Aware Task Offloading in Distributed Cloudlets with Virtual Network

发布者:计算机信息发布时间:2017-12-14浏览次数:258

  学术报告通知(编号:2017-48)

报告题目:QoS-Aware Task Offloading in Distributed Cloudlets with Virtual Network

报告人梁维发教授

单  位:Australian National University

报告时间:2017年12月20日(星期)下午2:30-4:00

报告地点逸夫楼508会议

报告人介绍

Prof. Weifa Liang received his PhD degree from the Australian National University in 1998, the Master of Engineering degree from the University of Science and Technology of China in 1989, and the BSc degree from Wuhan University, China in 1984, all in computer science. He is currently a Full Professor in the Research School of Computer Science at the Australian National University. His research interests include design and analysis of energy efficient routing protocols for wireless ad hoc and sensor networks, cloud computing, Virtual Network Function Placement, Software-Defined Networking, graph databases, design and analysis of parallel and distributed algorithms, approximation algorithms, combinatorial optimization, and graph theory. He has published over 200 journal and conference papers in top venues. He is a senior member of the IEEE.

报告摘要:

Pushing the cloud frontier to the network edge has attracted tremendous interest not only from cloud operators of the IT service/software industry but also from network service operators that provide various network services for mobile users. In particular, by deploying cloudlets in metropolitan area networks, network service providers can provide various network services through implementing virtualized network functions to meet the demands of mobile users. In this paper we formulate a novel task offloading problem in a metropolitan area network, where each offloaded task requests a specific network function with a maximum tolerable delay and different offloading requests may require different network services. We aim to maximize the number of requests admitted while minimizing their admission cost within a finite time horizon. We first show that the problem is NP-hard, and then devise an efficient algorithm through reducing the problem to a series of minimum weight maximum matching in auxiliary bipartite graphs. We also consider dynamic changes of offloading request patterns over time, and develop an effective prediction mechanism to release and/or create instances of network functions in different cloudlets for cost savings. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results indicate that the proposed algorithms are promising.