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Efficient algorithms for delay-aware NFV-Enabled multicasting in mobile edge clouds with resource sharing

Ren, Haozhe, Xu, Zichuan, Liang, Weifa, Xia, Qiufen, Zhou, Pan, Rana, Omer F., Galis, Alex and Wu, Guowei 2020. Efficient algorithms for delay-aware NFV-Enabled multicasting in mobile edge clouds with resource sharing. IEEE Transactions on Parallel and Distributed Systems 31 (9) , pp. 2050-2066. 10.1109/TPDS.2020.2983918

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Abstract

Stringent delay requirements of many mobile applications have led to the development of mobile edge clouds, to offer low latency network services at the network edges. Most conventional network services are implemented via hardware-based network functions, including firewalls and load balancers, to guarantee service security and performance. However, implementing hardware-based network functions usually incurs both a high capital expenditure (CAPEX) and operating expenditure (OPEX). Network Function Virtualization (NFV) exhibits a potential to reduce CAPEX and OPEX significantly, by deploying software-based network functions in virtual machines (VMs) on edge-clouds. We consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request has both service function chain and end-to-end delay requirements. Specifically, each multicast request requires chaining of a sequence of network functions (referred to as a service function chain) from a source to a set of destinations within specified end-to-end delay requirements. We devise an approximation algorithm with a provable approximation ratio for a single multicast request admission if its delay requirement is negligible; otherwise, we propose an efficient heuristic. Furthermore, we also consider admissions of a given set of the delay-aware NFV-enabled multicast requests, for which we devise an efficient heuristic such that the system throughput is maximized, while the implementation cost of admitted requests is minimized. We finally evaluate the performance of the proposed algorithms in a real test-bed, and experimental results show that our algorithms outperform other similar approaches reported in literature.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1045-9219
Date of First Compliant Deposit: 1 April 2020
Date of Acceptance: 30 March 2020
Last Modified: 10 Jun 2020 13:11
URI: http://orca.cf.ac.uk/id/eprint/130723

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