4.7 Article

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

期刊

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2020.2970048

关键词

Mobile edge computing (MEC); virtualized network function (VNF); virtualized service functions (VNFs); revenue maximization; reliability-aware service provisioning; Fault-tolerance; software failure; cloudlet failure; online algorithms; the primal-dual dynamic updating technique; mobile edge-cloud networks; competitive ratio analysis; resource allocation and optimization

资金

  1. Australian Research Council [DP200101985]
  2. Research Grants Council of Hong Kong [CityU11214316]
  3. Australian Research Council [DP200101985] Funding Source: Australian Research Council

向作者/读者索取更多资源

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. In this article, 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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