Replica Placement in Data Grid: A Multi-objective Approach

One of the challenges in data replication is to select the candidate sites to place replicas. We use the p-median and p-center models to address the replica placement problem. In our problem, the p-median model finds the locations of p candidate sites to place a replica to optimize the total (or average) response time. The p-center model hosts replicas to p candidate sites by minimizing the maximum response time among sites. A Grid environment is highly dynamic so placing a replica by considering one objective, i.e., optimize average response time or optimize maximum response time, may not be always a good choice. We propose a multi-objective model that considers the objectives of p-median and p-center simultaneously to select the candidate sites that will host replicas. Simulation results demonstrate that the multi-objective model outperforms single objective models in dynamic environments such as Data Grids.

[1]  Peter Z. Kunszt,et al.  Giggle: A Framework for Constructing Scalable Replica Location Services , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[2]  David Abramson,et al.  Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.

[3]  S. L. Hakimi,et al.  Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph , 1964 .

[4]  C. Reeves Modern heuristic techniques for combinatorial problems , 1993 .

[5]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[6]  Reda Alhajj,et al.  Replica placement in data grid: considering utility and risk , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[7]  Min Cai,et al.  A Peer-to-Peer Replica Location Service Based on a Distributed Hash Table , 2004, Proceedings of the ACM/IEEE SC2004 Conference.

[8]  Ian T. Foster,et al.  Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing , 2001, 2001 Eighteenth IEEE Symposium on Mass Storage Systems and Technologies.

[9]  Kurt Stockinger,et al.  OptorSim-A Grid Simulator for Studying Dynamic Data Replication Strategies , 2003 .

[10]  Kavitha Ranganathan,et al.  Decoupling computation and data scheduling in distributed data-intensive applications , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[11]  Stephen A. Marglin Public Investment Criteria , 1967 .

[12]  Kavitha Ranganathan,et al.  Improving Data Availability through Dynamic Model-Driven Replication in Large Peer-to-Peer Communities , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[13]  Ian Foster Internet Computing and the Emerging Grid , 2000 .

[14]  Zvi Drezner,et al.  Facility location - applications and theory , 2001 .

[15]  Marshall L. Fisher,et al.  The Lagrangian Relaxation Method for Solving Integer Programming Problems , 2004, Manag. Sci..

[16]  Yu Hu,et al.  GRESS - a Grid Replica Selection Service , 2003, ISCA PDCS.

[17]  Charles S. ReVelle,et al.  The Location of Emergency Service Facilities , 1971, Oper. Res..

[18]  Mark S. Daskin,et al.  Network and Discrete Location: Models, Algorithms and Applications , 1995 .

[19]  Ian T. Foster,et al.  The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets , 2000, J. Netw. Comput. Appl..