The rapid growth of time-critical information services and business-oriented applications is making quality of service (QoS) support increasingly important in content distribution. This paper investigates the problem of placing object replicas (e.g., web pages and images) to meet the QoS requirements of clients with the objective of minimizing the replication cost. We consider two classes of service models: replica-aware service and replica-blind service. In the replica-aware model, the servers are aware of the locations of replicas and can therefore direct requests to the nearest replica. We show that the QoS-aware placement problem for replica-aware services is NP-complete. Several heuristic algorithms for efficient computation of suboptimal solutions are proposed and experimentally evaluated. In the replica-blind model, the servers are not aware of the locations of replicas or even their existence. As a result, each replica only serves the requests flowing through it under some given routing strategy. We show that there exist polynomial optimal solutions to the QoS-aware placement problem for replicablind services. Efficient algorithms are proposed to compute the optimal locations of replicas under different cost models.

Abstract—The rapid growth of new information services and business-oriented applications entails the consideration of quality of service (QoS) in content distribution. This paper investigates the QoS-aware replica placement problems for responsiveness QoS requirements. We consider two classes of service models: replica-aware services and replica-blind services. In replica-aware services, the servers are aware of the locations of replicas and can therefore optimize request routing to improve responsiveness. We show that the QoS-aware placement problem for replica-aware services is NP-complete. Several heuristic algorithms for fast computation of good solutions are proposed and experimentally evaluated. In replica-blind services, the servers are not aware of the locations of replicas or even their existence. As a result, each replica only serves the requests flowing through it under some given routing strategy. We show that there exist polynomial optimal solutions to the QoS-aware placement problem for replica-blind services. Efficient algorithms are proposed to compute the optimal locations of replicas under different cost models. Index Terms—Content distribution, replication, placement, quality of service, dynamic programming, NP-complete. 1

The problem of placing replicated servers with QoS constraints is considered. Each server site may consist of multiple server types with varying capacities and each site can be placed in any location among those belonging to a given set. Each client can de served by more than one locationsaslongastherequestround-tripdelaysatisfies predetermined upper bounds. Our main focus is to minimize the cost of using the servers and utilizing the link bandwidth, while serving requests according to their delay constraint. This is an NP-hard problem. A pseudopolynomial and a polynomial algorithm that provide guaranteed approximation factors with respect to the optimal for the problem at hand are presented. 1

Most important commercial Web sites maintain multiple replicas of their server infrastructure to increase both reliability and performance. In this paper, we study how many replicas should be used and where they should be placed in order to improve client network performance, including both the latency (e.g., round-trip time) between clients and the replicas, and the bandwidth performance between them. This study is based on a large scale measurement study from an 18-node infrastructure, which reveals for the first time the distribution of today's Internet end-user access bandwidth. For example, we find that 50% of end users have access bandwidth less than 4.2Mbps. Using a greedy algorithm, we show that the first five replicas dominate latency optimization in our measurement infrastructure, while the first two replicas dominate bandwidth optimization. We also found that geographic diversity does not help as much for bandwidth optimization as it does for latency. To determine the proper trade-off between latency and bandwidth, we use a simplified TCP model to show that, when content size is less than 10KB, the deployment should focus on optimizing latency, while for content sizes larger than 1MB, the deployment should focus on optimizing bandwidth.

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