Decentralized Topology Aggregation for QoS Estimation in Large Overlay Networks

Abstract

This paper introduces a scalable approach for efficient, low-cost multi-level Quality of Service (QoS) estimation in large overlay networks (ON). We modify an existing distributed partitioning algorithm, and use it to create "QoS maps". QoS maps empower applications to quickly predict several QoS metrics for any given route, and to obtain multiple alternative routes to any target node in the ON. We show that our modifications of the partitioning algorithm permit the aggregation of large hubs, but still preserve the sub-linear runtime of the original heuristic. Simulations with large ONs are performed to evaluate the proposed approach and demonstrate its scalability. Finally, we outline our estimation algorithm that we use to predict QoS and perform QoS aware routing in any given ON.