Elastic Scaling of Stateful Network Functions

TitleElastic Scaling of Stateful Network Functions
Publication TypeBook Chapter
Year of Publication2018
AuthorsWoo, S., Sherry J., Han S., Moon S., Ratnasamy S., & Shenker S.
Published inProceedings of NSDI '18

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



We thank our shepherd Timothy Roscoe and the anonymous reviewers for their invaluable comments. We also thank Aurojit Panda for the enjoyable discussions and feedback, Keunhong Lee and Junmin Choe for their help on evaluations. This work was funded in part by NRF2014R1A2A1A01007580, NSF-1553747, NSF-1704941 and Intel corporation.

ICSI Research Group

Networking and Security