Computational and data Grids in large-scale science and engineering
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Evaluation of an Economy-Based File Replication Strategy for a Data Grid
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Simulation of Dynamic Data Replication Strategies in Data Grids
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
An efficient replicated data access approach for large-scale distributed systems
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Wide Area Data Replication for Scientific Collaborations
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Liana: a decentralized load-dependent scheduler for performance-cost optimization of grid service
The Journal of Supercomputing
A model to predict the optimal performance of the Hierarchical Data Grid
Future Generation Computer Systems
Optimizing server placement in distributed systems in the presence of competition
Journal of Parallel and Distributed Computing
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In this paper, we address some problems related to server placement in Grid environments. Given a hierarchical network with requests from clients and constraints on server capability, the minimum server placement problem attempts to place the minimum number of servers that satisfy requests from clients. Instead of using a heuristic approach, we propose an optimal algorithm based on dynamic programming to solve the problem. We also consider the balanced server placement problem, which tries to place a given number of servers appropriately so that their workloads are as balanced as possible. We prove that an optimal server placement can be achieved by combining the above algorithm with a binary search on workloads. This approach can be further extended to deal with constrains on network capability. The simulation results clearly show the improvement in the number of servers and the maximum workload. Furthermore, as the maximum workload is reduced, the waiting time is reduced accordingly.