Name
Abstract | ||
|---|---|---|
While the industry is still struggling to embrace the network function virtualization paradigm, recently a novel approach has appeared with the promise of improving the state-of-the-art: stateless virtualized network functions. Rooted in cloud-native computing, this design outsources the state embedded in virtual network functions to a dedicated "state storage" layer, facilitating elastic scaling and resiliency. While related work mostly focuses on performance, we in this paper pinpoint all other factors that weigh in when it comes to deploying the stateless design in a carrier-grade operator network. Among those we argue that reliability and flexibility are key, and we propose a system design that can be adapted to any telco use case without the need for complex coordination among the network control, the stateless network functions, and the state storage backend. Then, in extensive evaluations on synthetic use cases we show that the additional flexibility provided by our design does not come at a performance penalty; in fact, in certain cases our design outperforms the state-of-the-art significantly. Finally, we present what to our knowledge is the first product-phase realization of the stateless paradigm, an operational virtualized IP Multimedia Subsystem that can restore the live call records of thousands of mobile subscribers under a couple of seconds with half the resources required by a traditional "stateful" design. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CLOUD.2019.00068 | 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) |
Keywords | Field | DocType |
Network Function Virtualization,Stateless Network Function,Distributed Key-Value Store | Psychological resilience,Virtual network,Use case,Computer science,Systems design,Operator (computer programming),Stateful firewall,IP Multimedia Subsystem,Stateless protocol,Distributed computing | Conference |
ISSN | ISBN | Citations |
2159-6182 | 978-1-7281-2706-4 | 1 |
PageRank | References | Authors |
0.38 | 6 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Márk Szalay | 1 | 11 | 5.59 |
| Máté Nagy | 2 | 1 | 0.38 |
| Daniel Gehberger | 3 | 1 | 0.38 |
| Zoltán Lajos Kis | 4 | 46 | 4.84 |
| Péter Mátray | 5 | 3 | 2.21 |
| Felician Németh | 6 | 90 | 13.42 |
| Gergely Pongrácz | 7 | 68 | 16.25 |
| Gábor Rétvári | 8 | 194 | 24.87 |
| László Toka | 9 | 55 | 14.49 |