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Fairness-Aware VNF Sharing and Rate Coordination for High Efficient Service Scheduling

Network service provisioning becomes flexible and programmable with the help of Network Function Virfitualization (NFV), since NFV abstracts various service functions into software components called Virtual Network Function (VNF) and… Click to show full abstract

Network service provisioning becomes flexible and programmable with the help of Network Function Virfitualization (NFV), since NFV abstracts various service functions into software components called Virtual Network Function (VNF) and VNFs can be flexibly and quickly composed to form new services. It is commonly known that sharing the same VNF among different services can improve the resource utilization. However, we should be aware that such sharing also leads to serious resource preemption. In addition, VNF sharing aggravates the generation of the performance bottleneck, which then causes the rate mismatch problem between the upstream and downstream VNFs belonging to the same service chain. In this article, we propose a dynamic and flexible algorithm to jointly address the VNF sharing resource allocation and the rate coordination between the upstream and downstream VNFs. Specifically, 1) the VNFs are shared among different service chains with a fairness factor considered for the purpose of reducing the resource preemption probability and improving the resource utilization; 2) the backpressure indicator of each VNF is defined to judge its pressure condition, based on which we can dynamically adjust the processing rates between it and its downstream or upstream VNFs by maximizing the idle resource utilization. The experimental results indicate that the proposed algorithm outperforms the other methods in terms of the average delay, the flow completion time, the throughput and the backlog, etc. Meanwhile, the proposed algorithm achieves more stable performance than the other methods.

Keywords: service; vnf sharing; resource; rate coordination

Journal Title: IEEE Transactions on Parallel and Distributed Systems
Year Published: 2022

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