IEEE/ACM Transactions on Networking (TON)
Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
BRITE: An Approach to Universal Topology Generation
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
QoS-Aware Replica Placement for Content Distribution
IEEE Transactions on Parallel and Distributed Systems
Optimizing Network Performance In Replicated Hosting
WCW '05 Proceedings of the 10th International Workshop on Web Content Caching and Distribution
Designing cost-effective content distribution networks
Computers and Operations Research
Replicated server placement with qos constraints
QoS-IP'05 Proceedings of the Third international conference on Quality of Service in Multiservice IP Networks
Object replication strategies in content distribution networks
Computer Communications
Replicating for performance: case studies
Replication
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The replica server placement problem determines the optimal location where replicated servers should be placed in content distribution networks, in order to optimize network performance. The estimated traffic demand is fundamental input to this problem and its accuracy is essential for the target performance to be achieved. However, deriving accurate traffic demands is far from trivial and uncertainty makes the target performance hard to predict. We argue that it is often inappropriate to optimize the performance for only a particular set of traffic demands that is assumed accurate. In this paper, we propose a scenario-based robust optimization approach to address the replica server placement problem under traffic demand uncertainty. The objective is to minimize the total distribution cost across a variety of traffic demand scenarios while minimizing the performance deviation from the optimal solution. Empirical results demonstrate that robust optimization for replica server placement can achieve good performance under all the traffic demand scenarios while non-robust approaches perform significantly worse. This approach allows content distribution providers to provision better and predictable quality of service for their customers by reducing the impact of inaccuracy in traffic demand estimation on the replica server placement optimization.