Abstract
Abstract In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several ‘experts’ guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT. Through extensive simulations, we show that the proposed machine-learning algorithm adapts very quickly to changes in the RTT. Our results show a considerable reduction in the number of retransmitted packets and an increase in goodput, especially in more heavily congested scenarios. We corroborate our results through ‘live’ experiments using an implementation of the proposed algorithm in the Linux kernel. These experiments confirm the higher RTT estimation accuracy of the machine learning approach which yields over 40% improvement when compared against both standard transmission control protocol (TCP) as well as the well known Eifel RTT estimator. To the best of our knowledge, our work is the first attempt to use on-line learning algorithms to predict network performance and, given the promising results reported here, creates the opportunity of applying on-line learning to estimate other important network variables.
Highlights
Latency is an important parameter when designing, managing, and evaluating computer networks, their protocols, and applications
Through extensive simulations and live experiments, we show that our machine learning technique can adapt to changes in the round-trip time (RTT) faster and predict its value more accurately than the current exponential weighted moving average (EWMA) technique employed by most versions of transmission control protocol (TCP)
We focus on the first part of the problem, i.e., the prediction of the RTT; the second part of the problem, i.e., setting the retransmission time-out (RTO), is the focus of future work
Summary
Latency is an important parameter when designing, managing, and evaluating computer networks, their protocols, and applications. Depending on how the RTT is measured (e.g., at which layer of the protocol stack), besides the time it takes for the data to be serviced by the network, the RTT accounts for the ‘service time’ at the communication end points. RTT measurement can be done implicitly by using existing messages; in several instances, explicit ‘probe’ messages have to be used. Such explicit measurement techniques can render the RTT estimation process quite expensive in terms of their communication and computational burden.
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