Abstract
Resource allocation is one of the principal stages of relational query processing in data grid systems. Static allocation methods allocate nodes to relational operations during query compilation. Existing heuristics did not take into account the multi-queries environment, where some nodes may become overloaded because they are allocated to too many concurrent queries. Dynamic resource allocation mechanisms are currently developed to modify the physical plan during query execution. In fact, when a node is detected to be overloaded, some of the operations on it will migrate. However, if the resource contention is too heavy in the initial execution plan, the operation migration cost may be very high. In this paper, we propose two load balancing strategies adopted during the static resource allocation phase, so that the workload is balanced at the beginning, the operation migration cost is decreased during the query execution, and therefore the average response time is reduced.
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References
Chervenak, A., et al.: 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 (1999)
Smith, J., Gounaris, A., Watson, P., Paton, N.W., Fernandes, A.A.A., Sakellariou, R.: Distributed Query Processing on the Grid. In: Parashar, M. (ed.) GRID 2002. LNCS, vol. 2536, pp. 279–290. Springer, Heidelberg (2002)
Krauter, K., et al.: A taxonomy and survey of grid resource management systems for distributed computing. Journal of Software: Practice and Experience 32, 135–164 (2002)
Gounaris, A., et al.: Resource scheduling for parallel query processing on computational grids. In: GRID (2004)
Soe, K.M., et al.: Efficient scheduling of resources for parallel query processing on grid-based architecture. In: Information and Telecommunication Technologies (2005)
Liu, S., Karimi, H.A.: Grid query optimizer to improve query processing in grids. Future Gener. Comput. Syst. 24, 342–353 (2008)
Epimakhov, I., et al.: GeoLoc: Robust Resource Allocation Method for Query Optimization in Data Grid Systems. In: DB&IS (2012)
Gounaris, A., et al.: Adaptive query processing and the grid: Opportunities and challenges. In: DEXA Workshops (2004)
Gounaris, A., et al.: Practical adaptation to changing resources in grid query processing. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006 (2006)
Da Silva, V.F.V., et al.: An adaptive parallel query processing middleware for the grid. Concurrency and Computation: Practice and Experience 18(6), 621–634 (2006)
Avnur, R., Hellerstein, J.M.: Eddies: Continuously adaptive query processing. In: Proceedings of the SIGMOD Conference, pp. 261–272 (2000)
Patni, J., et al.: Load balancing strategies for grid computing. In: Proceedings of the 3rd International Conference on Electronics Computer Technology, ICECT (2011)
Epimakhov, I., et al.: Mobile Agent-based Dynamic Resource Allocation Method for Query Optimization in Data Grid Systems. In: International KES Conference on Agents and Multi-agent Systems – Technologies and Applications (2013)
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Yin, S., Epimakhov, I., Morvan, F., Hameurlain, A. (2013). Resource Allocation for Query Optimization in Data Grid Systems: Static Load Balancing Strategies. In: Catania, B., Guerrini, G., Pokorný, J. (eds) Advances in Databases and Information Systems. ADBIS 2013. Lecture Notes in Computer Science, vol 8133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40683-6_24
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DOI: https://doi.org/10.1007/978-3-642-40683-6_24
Publisher Name: Springer, Berlin, Heidelberg
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