Using the Last-Mile Model as a Distributed Scheme for Available Bandwidth Prediction

Abstract

Several Network Coordinate Systems have been proposed to predict unknown network distances between a large number of Internet nodes by using only a small number of measurements. These systems focus on predicting latency, and they are not adapted to the prediction of available bandwidth. But end-to-end path available bandwidth is an important metric for the performance optimisation in many high throughput distributed applications, such as video streaming and file sharing networks. In this paper, we propose to perform available bandwidth prediction with the last-mile model, in which each node is characterised by its incoming and outgoing capacities. This model has been used in several theoretical works for distributed applications. We design decentralised heuristics to compute the capacities of each node so as to minimise the prediction error. We show that our algorithms can achieve a competitive accuracy even with asymmetric and erroneous end-to-end measurement datasets. A comparison with existing models (Vivaldi, Sequoia, PathGuru, DMF) is provided. Simulation results also show that our heuristics can provide good quality predictions even when using a very small number of measurements.

Decentralized Network Bandwidth Prediction and Node Search

“…Unlike our approach, PathGuru is a landmark-based system. More importantly, PathGuru provides quite poor accuracy [19], even worse than Sequoia. An ultrametric space (V, d) satisfies the three-point condition (3PC) that states that for any set of three nodes x, y, z ∈ V ,…”

“…Although DMF is initially designed for latency prediction, one study [19] shows that DMF can predict network bandwidth more accurately than coordinate-based approaches. Despite its successful bandwidth prediction, DMF has two downsides compared to our approach.…”

“…Last-mile [19] is a decentralized system that predicts end-to-end network bandwidth, and shows higher accuracy than Sequoia [58] and Vivaldi [26]. Last-mile is based on the assumption of an edge-bandwidth network model that is described in Chapter 2.…”

Broadcasting on large scale heterogeneous platforms under the bounded multi-port model

“…Another special case is when only one red node is left. In that case (see lines [9][10][11][12][13][14], the algorithm chooses at each step the node with the largest b i (unless it is red and G(π) < T ).…”

“…In the case of latencies, the most well known embedding tools are Vivaldi [12], which embeds nodes into a 2D+1 metric space and relies on direct measurements to adapt dynamically node coordinates, and Sequoia [13], which embeds the nodes as the leaves of a weighted tree and relies on the distance in the tree to estimate the latency. Both coordinate systems have been extended to estimate bandwidths in [13], but it has been recently proved experimentally [14] on the PlanetLab dataset that the basic LastMile or bounded multiport model, where each node is associated to a incoming and an outgoing bandwidth limit, and where the achievable bandwidth between C i and C j is the minimum of the outgoing bandwidth of C i and the incoming bandwidth of C j , is more accurate than those more sophisticated models with respect to bandwidth prediction.…”