NetClust: A Framework for Scalable and Pareto-Optimal Media Server Placement

Abstract

Effective media server placement strategies are critical for the quality and cost of multimedia services. Existing studies have primarily focused on optimization-based algorithms to select server locations from a small pool of candidates based on the entire topological information and thus these algorithms are not scalable due to unavailability of the small pool of candidates and low-efficiency of gathering the topological information in large-scale networks. To overcome this limitation, a novel scalable framework called NetClust is proposed in this paper. NetClust takes advantage of the latest network coordinate technique to reduce the workloads when obtaining the global network information for server placement, adopts a new K-means-clustering-based algorithm to select server locations and identify the optimal matching between clients and servers. The key contribution of this paper is that the proposed framework optimizes the trade-off between the service delay performance and the deployment cost under the constraints of client location distribution and the computing/storage/bandwidth capacity of each server simultaneously. To evaluate the performance of the proposed framework, a prototype system is developed and deployed in a real-world large-scale Internet. Experimental results demonstrate that 1) NetClust achieves the lower deployment cost and lower delay compared to the traditional server selection method; and 2) NetClust offers a practical and feasible solution for multimedia service providers.