Data Management in an International Data Grid Project
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
Identifying Dynamic Replication Strategies for a High-Performance Data Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
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
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Optimizing server placement for QoS requirements in hierarchical grid environments
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive 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 clients requests. 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 of workloads. We extend this approach to deal with constrains on network capability. The simulation results clearly show an improvement in the number of servers and the maximum workload. Furthermore, as the maximum workload is reduced, the waiting times are reduced accordingly.