Optimizing server placement in hierarchical grid environments
- 31 Downloads
- 4 Citations
Abstract
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.
Keywords
File replication Grids Queueing systems Server placement Waiting time WorkloadPreview
Unable to display preview. Download preview PDF.
References
- 1.Abawajy JH (2004) Placement of file replicas in data grid environments. In: International conference on computational science, 2004, pp 66–73 Google Scholar
- 2.Bell WH, Cameron DG, Carvajal-Schiaffino R, Millar AP, Stockinger K, Zini F (2003) Evaluation of an economy-based file replication strategy for a data grid. In: International workshop on agent based cluster and grid computing at CCGrid 2003, May 2003, pp 120–126 Google Scholar
- 3.BIRN: the biomedical informatics research network. http://www.nbirn.net
- 4.Chervenak A, Foster I, Kesselman C, Salisbury C, Tuecke S (2000) The data grid: towards an architecture for the distributed management and analysis of large scientific datasets. J Netw Comput Appl 23(3):187–200 CrossRefGoogle Scholar
- 5.Chervenak A, Schuler R, Kesselman C, Koranda S, Moe B (2005) Wide area data replication for scientific collaborations. In: SC’05: Proc the 6th IEEE/ACM international workshop on grid computing CD, Seattle, Washington, USA, IEEE/ACM, Nov 2005, pp 1–8 Google Scholar
- 6.Deelman E, Kesselman C, Mehta G, Meshkat L, Pearlman L, Blackburn K, Ehrens P, Lazzarini A, Williams R, Koranda S (2002) GriPhyN and LIGO, building a virtual data grid for gravitational wave scientists. In: HPDC 2002 Google Scholar
- 7.Deris MM, Abawajy JH, Suzuri HM (2004) An efficient replicated data access approach for large-scale distributed systems. In: CCGRID, 2004, pp 588–594 Google Scholar
- 8.EU DataGrid. http://www.edg.org
- 9.Foster IT, Kesselman C, Tuecke S (2001) The anatomy of the grid: enabling scalable virtual organizations. Int J High Perform Comput 15(3) Google Scholar
- 10.Grid Physics Network (GriphyN). http://www.griphyn.org
- 11.Hoschek W, Jaén-Martínez FJ, Samar A, Stockinger H, Stockinger K (2000) Data management in an international data grid project. In: GRID 2000, pp 77–90 Google Scholar
- 12.iVDGL: international virtual data grid laboratory. http://www.ivdgl.org
- 13.Johnston WE (2002) Computational and data grids in large-scale science and engineering. Future Gener Comput Syst 18(8):1085–1100 MATHCrossRefGoogle Scholar
- 14.Lamehamedi H, Shentu Z, Szymanski BK, Deelman E (2003) Simulation of dynamic data replication strategies in data grids. In: IPDPS 2003, p 100 Google Scholar
- 15.PPDG: particle physics data grid. http://www.ppdg.net
- 16.Ranganathan K, Foster IT (2001) Identifying dynamic replication strategies for a high-performance data grid. In: GRID 2001, pp 75–86 Google Scholar
- 17.Ranganathan K, Iamnitchi A, Foster IT (2002) Improving data availability through dynamic model-driven replication in large peer-to-peer communities. In: CCGRID, 2002, pp 376–381 Google Scholar