Erlang-k-based packet latency prediction model for optimal configuration of software routers

Providing the optimal configuration for a software router poses a lot of technical challenges that do not present in the dedicated hardware router. One of them is how to characterize performance varying due to different configurations on commodity hardware. This paper addresses the problem of configuring a software router that provides the minimum of average packet latency. Since changing all combinations of hardware configurations of a software router for searching the optimum is cumbersome, we propose a prediction model to accurately estimate the packet latency of a software router. We first analyze the relationship of the packet latency distribution with the configured and observed parameters. Empirical measurements suggest that the Erlang-k distribution is a reasonable model for estimating the packet latency distribution. Motivated by the parameter relationship analysis, we propose a prediction model for packet latency of a software router based on the Erlang- k distribution. Our prediction model requires measurement of only two different configurations, i.e., one and two Rx queues of a network interface card, to predict the average packet latency of all combinations of configurations. We use the measured data from the testbed experiments and the data of curve fitting method to cross-verify the accuracy of our prediction model. Underlying the prediction model, we propose the optimal configuration selection (OCS) algorithm to justify which configuration yields the minimum of average packet latency. Our prediction model based OCS results in the same optimal configuration with the measured data based ones.