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Reliability-Aware Network Service Provisioning in Mobile Edge-Cloud Networks

计算机科学 供应 备份 云计算 计算机网络 虚拟网络 可靠性(半导体) 移动边缘计算 分布式计算 服务(商务) 云朵 边缘设备 服务提供商 服务器 操作系统 功率(物理) 经济 经济 量子力学 物理
作者
Jing Li,Weifa Liang,Meitian Huang,Xiaohua Jia
出处
期刊:IEEE Transactions on Parallel and Distributed Systems [Institute of Electrical and Electronics Engineers]
卷期号:31 (7): 1545-1558 被引量:72
标识
DOI:10.1109/tpds.2020.2970048
摘要

The Mobile Edge-Cloud (MEC) network has emerged as a promising networking paradigm to address the conflict between increasing computing-intensive applications and resource-constrained mobile Internet-of-Thing (IoT) devices with portable size and storage. In MEC environments, Virtualized Network Functions (VNFs) are deployed for provisioning network services to users to reduce the service cost on top of dedicated hardware infrastructures. However, VNFs may suffer from failures and malfunctions while network service providers have to guarantee continuously reliable services to their consumers to meet the ever-growing service demands of users, thereby securing their revenues for the service. We focus on reliable VNF service provisioning in MECs, by placing primary and backup VNF instances to cloudlets in an MEC network to meet the service reliability requirements of users. We first formulate a novel VNF service reliability problem with the aim to maximize the revenue collected by admitting as many as user requests while meeting their different reliability requirements, assuming that requests arrive into the system one by one without the knowledge of future arrivals, and the admission or rejection decision must be made immediately. We then develop two efficient online algorithms for the problem under two different backup schemes: the on-site (local) and off-site (remote) schemes, by adopting the primal-dual updating technique. Both algorithms achieve provable competitive ratios with bounded moderate resource capacity violations. We finally evaluate the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, compared with existing baseline algorithms.
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