Abstract
Data grids seek to harness geographically distributed resources for large-scale data-intensive problems. Such problems involve loosely coupled jobs and large data sets mostly distributed geographically. Data grids have found applications in scientific research, in the field of high-energy Physics, Life Sciences etc. The issues that need to be considered in the data grid research area include: resource management including computation management and data management. Computation management include scheduling of jobs, scalability, response time involved in such scheduling, while data management include data replication in selected sited, data movement when required. Therefore, scheduling and replication assumes great importance in a data grid environment. In this paper, we have developed several scheduling strategies based on a developed replication strategy. The scheduling strategies are called Matching based Scheduling (MJS), Cost base Scheduling (CJS) and Latency based Scheduling (LJS). Among these, LJS and CJS perform similarly and MJS performs worse than both of them.
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References
Chervenak, Foster, I., Kesselman, C., Salisbury, C., Tuecke, S.: The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Datasets. Journal of Network and Computer Applications 23, 187–200 (2001)
Foster, Kesselman, C.: The Globus Project: A Status Report. In: Proc. IPPS/SPDP 1998 Heterogeneous Computing Workshop, pp. 4–18 (1998)
Casanova, H., Obertelli, G., Berman, F., Wolski, R.: The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid. In: Proceedings of SuperComputing 2000 (2000)
Alhusaini, A.H., Prasanna, V.K., Raghavendra, C.S.: A Unified Resource Scheduling Framework for Heterogeneous Computing Environments. In: Eighth Heterogeneous Computing Workshop (1999)
Ranganathan, K., Foster, I.: Identifying dynamic replication strategies for a high-performance data grid. In: Lee, C.A. (ed.) GRID 2001. LNCS, vol. 2242, p. 75. Springer, Heidelberg (2001)
Chakrabarti, A., Dheepak, R.A., Sengupta, S.: Integration of Scheduling and Replication in Data Grids. In: Bougé, L., Prasanna, V.K. (eds.) HiPC 2004. LNCS, vol. 3296, pp. 375–385. Springer, Heidelberg (2004)
Bell, W.H., Cameron, D.G., Capozza, L., Millar, A.P., Stockinger, K., Zini, F.: Simulation of dynamic grid replication strategies in optorSim. In: Parashar, M. (ed.) GRID 2002. LNCS, vol. 2536, pp. 46–57. Springer, Heidelberg (2002)
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Dheepak, R.A., Ali, S., Sengupta, S., Chakrabarti, A. (2004). Study of Scheduling Strategies in a Dynamic Data Grid Environment. In: Sen, A., Das, N., Das, S.K., Sinha, B.P. (eds) Distributed Computing - IWDC 2004. IWDC 2004. Lecture Notes in Computer Science, vol 3326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30536-1_11
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DOI: https://doi.org/10.1007/978-3-540-30536-1_11
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