RTNF: Predictable Latency for Network Function Virtualization
Saeed Abedi, Neeraj Gandhi, Henri Maxime Demoulin, Li Yang, Yang Wu, Linh Thi Xuan Phan · 2019
A key challenge with network function virtualization is to provide stable latencies, so that the network functions can be treated simply as "bumps in the wire." In this paper, we present RTNF, a scalable framework for the online resource allocation and scheduling of NFV applications that provides predictable end-to-end latency guarantees. RTNF is based on a novel time-aware abstraction algorithm that transforms complex NFV graphs and their performance requirements into sets of scheduling interfaces; these can then be used by the resource manager and the scheduler on each node to efficiently allocate resources and to schedule NFV requests at runtime. We provide a complexity analysis of our algorithm and the design of a concrete implementation of our framework. Our evaluation, based on simulations and an experimental prototype, shows that RTNF can schedule DAG-based NFV applications with solid timing guarantees while incurring only a small overhead, and that it substantially outperforms existing techniques.