FACEST: feedback-assisted estimation of end-to-end capacity in IP-based communication networks

Authors: FATİH ABUT

Abstract: The end-to-end capacity, defined as the maximal transmission rate of the weakest link on the entire path between two end hosts, plays an important role in efficient network design and management. Although various capacity estimation tools have been proposed in the literature, there is still uncertainty in their accuracy and reliability when they are used in today's IP-based communication networks. The main reason for this is that all current capacity estimation tools only yield a potential candidate for an acceptable estimate, without being aware of its reliability level. In this study, we propose a new feedback-assisted end-to-end capacity estimation (FACEST) procedure that not only produces a candidate for a potentially acceptable estimate but also improves and categorizes its reliability level. Particularly, FACEST follows an ensemble estimation approach which meaningfully utilizes the correlation among the estimates produced by 3 independent capacity estimation tools; namely pathrate, DietTOPP and PBProbe. Through the correlation of 3 individual estimates, additional information about their reliability level is gained and, if necessary, the experiment is iteratively repeated with different sets of measurement parameter values until the required level of estimation accuracy is achieved, or in the worst case a kernel density estimator is applied on the collected experiment results. The proposed ensemble estimation approach has been implemented in a tool called FACEST, the performance of which has experimentally been evaluated on a three-hop testbed using a variety of tests with several scenarios and degrees of cross-traffic. For comparison purposes, individual experiments with pathrate, DietTOPP and PBProbe as well as with other alternative hybrid estimation tool from literature have also been conducted. The results reveal that FACEST outperforms individual and other hybrid capacity estimation tools and yields up to 18.29% lower estimation errors along with additional consistent information about the reliability level of the produced estimates.

Keywords: Capacity estimation, ensemble estimation, testbed, network measurement

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