GranularNF: Granular Decomposition of Stateful NFV at 100 Gbps Line Speed and Beyond

Ziyan Wu, Tianming Cui, Arvind Narayanan, Yang Zhang, Kangjie Lu, Antonia Zhai, Zhi Li Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we consider the challenges that arise from the need to scale virtualized network functions (VNFs) at 100 Gbps line speed and beyond. Traditional VNF designs are monolithic in state management and scheduling: internally maintaining all states and operations associated with them. Without proper design considerations, it suffers from limitations when scaling at 100 Gbps link speed and beyond: the inability of efficient utilization of the cache because of the contention due to the frequent control plane activities, computational/memory-intensive tasks taking up CPU times, shares states causing the synchronization among the cores. We address these limitations by arguing for the need to granularly decompose a VNF into data/control components that are co-located within a server but can be independently scaled among the cores. To realize the approach, we design a "serverless"programming framework with novel abstraction to optimize the data components that must process packets at the line speed, reduce the contention of the data states and enable run-time scheduling of different components for improved resource utilization. The abstractions, combined with the runtime system that we design, help NFV developers focus on the logic and correctness of VNF programming without worrying about how VNFs may be scaled in or out. We evaluate our platform by comparing it with monolithic approaches using different workloads and by analyzing its advantages of separation on scalability, performance determinism, and feature velocity.

Original languageEnglish (US)
Pages (from-to)46-51
Number of pages6
JournalPerformance Evaluation Review
Volume50
Issue number2
DOIs
StatePublished - Aug 30 2022

Bibliographical note

Funding Information:
We would like to thank the anonymous reviewers for their comments. The research was supported in part by NSF under Grants CNS-1814322, CNS-1831140, CNS-1836772, CNS-1901103, CNS-2106771, CNS-2045478 and CCF-2123987.

Publisher Copyright:
Copyright © 2022 is held by the owner/author(s).

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