CHESS: An application-aware space for enhanced scalable services in overlay networks

We introduce in this paper CHESS, an application-aware space for enhanced scalable (In our context, the scalability feature is satisfied when the system overhead is linear with the number of its nodes.) services in overlay networks. In this new space, the proximity of peers is determined according to a utility function that considers the network parameters (e.g., delay, bandwidth, and loss rate) impacting application performance. We motivate the need for this new notion by showing that the proximity in the delay space does not automatically lead to a proximity in another space (e.g., space of the bandwidth). For determining the proximity in CHESS, network parameters must be estimated easily and scalably. Therefore, we use the matrix factorization approach for estimating the delay and loss parameters. Besides, we propose a scalable model that estimates the bandwidth among peers using the bandwidth of the indirect paths that join them via a set of landmarks. Our idea is that an indirect path shares the same tight link with the direct path with a probability that depends on the location of the corresponding landmark with respect to the direct path or any of the two peers subject to bandwidth inference. The results show that characterizing the proximity in CHESS provides a much better quality than that obtained when using the delay proximity for large file transfer applications. The whole study is supported by real measurements carried out over Planetlab.