徐子川 (教授)

教授   博士生导师   硕士生导师

性别:男

毕业院校:澳大利亚国立大学

学位:博士

所在单位:软件学院、国际信息与软件学院

学科:软件工程

办公地点:开发区校区综合楼

联系方式:0411-62274514

电子邮箱:z.xu@dlut.edu.cn

Cost-Efficient NFV-Enabled Mobile Edge-Cloud for Low Latency Mobile Applications

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论文类型:期刊论文

第一作者:Yang, Binxu

通讯作者:Xu, ZC (reprint author), Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.

合写作者:Chai, Wei Koong,Xu, Zichuan,Katsaros, Konstantinos V.,Pavlou, George

发表时间:2018-03-01

发表刊物:IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT

收录刊物:SCIE、EI

卷号:15

期号:1

页面范围:475-488

ISSN号:1932-4537

关键字:Mobile edge-cloud; low latency applications; dynamic resource allocation; approximation algorithm

摘要:Mobile edge-cloud (MEC) aims to support low latency mobile services by bringing remote cloud services nearer to mobile users. However, in order to deal with dynamic workloads, MEC is deployed in a large number of fixed-location micro-clouds, leading to resource wastage during stable/low workload periods. Limiting the number of micro-clouds improves resource utilization and saves operational costs, but faces service performance degradations due to insufficient physical capacity during peak time from nearby micro-clouds. To efficiently support services with low latency requirement under varying workload conditions, we adopt the emerging network function virtualization (NFV)-enabled MEC, which offers new flexibility in hosting MEC services in any virtualized network node, e.g., access points, routers, etc. This flexibility overcomes the limitations imposed by fixed-location solutions, providing new freedom in terms of MEC service-hosting locations. In this paper, we address the questions on where and when to allocate resources as well as how many resources to be allocated among NFV-enabled MECs, such that both the low latency requirements of mobile services and MEC cost efficiency are achieved. We propose a dynamic resource allocation framework that consists of a fast heuristic-based incremental allocation mechanism that dynamically performs resource allocation and a reoptimization algorithm that periodically adjusts allocation to maintain a near-optimal MEC operational cost over time. We show through extensive simulations that our flexible framework always manages to allocate sufficient resources in time to guarantee continuous satisfaction of applications' low latency requirements. At the same time, our proposal saves up to 33% of cost in comparison to existing fixed-location MEC solutions.

发表时间:2018-03-01

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