e-approximations with minimum packing constraint violation (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Scheduling unrelated machines with costs
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Web caching using access statistics
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms for data placement in arbitrary networks
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Designing overlay multicast networks for streaming
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Near-optimal dynamic replication in unstructured peer-to-peer networks
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proactive data dissemination to mission sites
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Improved algorithms for data migration
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
Proactive data dissemination to mission sites
Wireless Networks
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We study the data placement problem [1, 3], where the goal is to place certain data objects (with possible replication) in fixed capacity caches in a network to optimize latency of access. The locations of the caches are given and each cache has capacities both on the number of objects it can store and the number of users it can serve. Each user has a demand for a specific object.The end objective is to optimize the average user latency of accessing the objects. We present a constant approximation, while blowing up the cache capacities by a constant factor. This improves the previous results, which either ignore the bound on the number of users [1], or which need to blow up the capacities by a logarithmic factor [3]. Our solution technique involves writing an integer program for this problem and rounding its linear relaxation.We note that our result is the best possible that can be obtained by LP rounding. The problem is MAX-SNP hard as shown in [1], and the linear program has unbounded integrality gap unless we relax the capacity constraints [5].Our basic technique is to separate the rounding into two stages:Opening Objects: In this stage, we consider each object separately, and open copies in the network. We ignore the interaction of this object with other objects due to the cache capacity constraints. We use the capacitated facility location rounding from [5].Packing Objects: In this stage, we pack the objects into the cache so that cache capacity constraints are satisfied. We use the GAP rounding from [4].