Visible to the public Efficient Monte Carlo Evaluation of SDN Resiliency

TitleEfficient Monte Carlo Evaluation of SDN Resiliency
Publication TypeConference Paper
Year of Publication2016
AuthorsNicol, David M., Kumar, Rakesh
Conference NameProceedings of the 2016 Annual ACM Conference on SIGSIM Principles of Advanced Discrete Simulation
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-3742-7
Keywordscps resiliency, fast fail-over, importance sampling, industrial control systems, Metrics, monte carlo, Neural Network, Neural networks, neural networks security, policy-based governance, pubcrawl, reliability, Resiliency, Scalability, SDN security, software defined networking
Abstract

Software defined networking (SDN) is an emerging technology for controlling flows through networks. Used in the context of industrial control systems, an objective is to design configurations that have built-in protection for hardware failures in the sense that the configuration has "baked-in" back-up routes. The objective is to leave the configuration static as long as possible, minimizing the need to have the controller push in new routing and filtering rules We have designed and implemented a tool that enables us to determine the complete connectivity map from an analysis of all switch configurations in the network. We can use this tool to explore the impact of a link failure, in particular to determine whether the failure induces loss of the ability to deliver a flow even after the built-in back-up routes are used. A measure of the original configuration's resilience to link failure is the mean number of link failures required to induce the first such loss of service. The computational cost of each link failure and subsequent analysis is large, so there is much to be gained by reducing the overall cost of obtaining a statistically valid estimate of resiliency. This paper shows that when analysis of a network state can identify all as-yet-unfailed links any one of whose failure would induce loss of a flow, then we can use the technique of importance sampling to estimate the mean number of links required to fail before some flow is lost, and analyze the potential for reducing the variance of the sample statistic. We provide both theoretical and empirical evidence for significant variance reduction.

URLhttp://doi.acm.org/10.1145/2901378.2901401
DOI10.1145/2901378.2901401
Citation Keynicol_efficient_2016