Elastic Scaling of Stateful Network Functions


Shinae Woo, KAIST, UC Berkeley; Justine Sherry, CMU; Sangjin Han, UC Berkeley; Sue Moon, KAIST; Sylvia Ratnasamy, UC Berkeley; Scott Shenker, UC Berkeley, ICSI


Elastic scaling is a central promise of NFV but has been hard to realize in practice. The difficulty arises because most Network Functions (NFs) are stateful and this state need to be shared across NF instances. Implementing state sharing while meeting the throughput and latency requirements placed on NFs is challenging and, to date, no solution exists that meets NFV’s performance goals for the full spectrum of NFs.

S6 is a new framework that supports elastic scaling of NFs without compromising performance. Its design builds on the insight that a distributed shared state abstraction is well-suited to the NFV context. We organize state as a distributed shared object (DSO) space and extend the DSO concept with techniques designed to meet the need for elasticity and high-performance in NFV workloads. S6 simplifies development: NF writers program with no awareness of how state is distributed and shared. Instead, S6 transparently migrates state and handles accesses to shared state. In our evaluation, compared to recent solutions for dynamic scaling of NFs, S6 improves performance by 100x during scaling events [25], and by 2-5x under normal operation


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@inproceedings {211293,
author = {Shinae Woo and Justine Sherry and Sangjin Han and Sue Moon and Sylvia Ratnasamy and Scott Shenker},
title = {Elastic Scaling of Stateful Network Functions},
booktitle = {15th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 18)},
year = {2018},
isbn = {978-1-931971-43-0},
address = {Renton, WA},
pages = {299--312},
url = {https://www.usenix.org/conference/nsdi18/presentation/woo},
publisher = {{USENIX} Association},