FlexSFC: Flexible Resource Allocation and VNF Parallelism for Improved SFC Placement

Agarwal, Sagar; Chintapalli, Venkatarami Reddy; Tamma, Bheemarjuna Reddy

doi:10.1109/netsoft54395.2022.9844084

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…This section is related to DPC validation in comparison with some benchmark methods such as FlexSFC, 25 OSIR, 27 DASM, 28 PPC, 29 and DSPPR 2 . All these methods are implemented using the MATLAB R2021 simulator and their results are reported on the same environment.…”

Section: Resultsmentioning

confidence: 99%

“…We compare DPC in various aspects compared to some existing benchmark methods. These methods are as follows: FlexSFC: Flexible Service Function Chain presented by Agarwal et al 25 OSIR: Online SFC placement combined with Instance Reuse presented by Zhang et al 27 DASM: Delay‐Aware Traffic Scheduling Mechanism presented by Huang et al 28 PPC: Partial Parallel Chaining presented by Lin et al 29 DSPPR: Dynamic SFC Placement scheme with Parallelized SFCs and the Reuse of initialized VNFs presented by Xu et al 2 …”

Section: Resultsmentioning

confidence: 99%

“…The objectives considered in this method include data rate and latency. Agarwal et al 25 focused on the problem of network function parallelism in edge computing environments and proposed the FlexSFC algorithm. As a flexible SFC placement algorithm, FlexSFC can solve the SFCP problem by parallelizing VNFs and improving resource utilization.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic service function chains placement based on parallelized requests in edge computing environment

Guo,

Rezaeipanah

2023

Trans Emerging Tel Tech

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The advent of Network Function Virtualization (NFV) technology brings flexible traffic engineering to edge computing environments. Online services in NFV are chained as Service Function Chains (SFCs), which consist of ordered sequences of Virtual Network Functions (VNFs). The SFC Placement (SFCP) problem is solved under Quality of Service (QoS) requirements and limited resource availability by directing traffic to the required VNFs. However, SFC assembly leads to high latency and network congestion by increasing the count of VNFs, which parallelized SFC can overcome this problem. With parallelizing an SFC request, independent VNFs are activated simultaneously and computational acceleration is realized by reducing the SFC length. Any pair of VNFs that do not conflict with traffic can be activated simultaneously. Most VNFs are deployed on distributed servers for load balancing, which makes SFC parallelization challenging. Meanwhile, the cost of merging/duplicating packets for parallelized SFCs between different servers is not negligible. Hence, in this article, Distributed Parallel Chaining (DPC) is proposed which is an algorithm based on Deep Reinforcement Learning (DRL) approaches. The DPC algorithm solves the SFCP problem to maximize the Long‐Term Expected Cumulative Reward (LTECR). DPC incorporates an Asynchronous Advantage Actor‐Critic (A3C) algorithm as a new approach based on DRL to increase the admission ability of future SFC requests by maximizing LTECR. The evaluation results show the effectiveness of the proposed algorithm from different aspects. Specifically, compared to the best existing approaches, DPC can reduce SFC latency by 8.7%.

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