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Time-sensitive resource re-allocation strategy for interdependent continuous tasks

Part of: EUMAS '15 - '16

Published online by Cambridge University Press:  22 July 2019

Valeriia Haberland ,
Simon Miles  and
Michael Luck
Valeriia Haberland
Affiliation:
MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; e-mail: [email protected]
Simon Miles
Affiliation:
Department of Informatics, King’s College London, London WC2R 2LS, UK; e-mail: [email protected]; [email protected]
Michael Luck
Affiliation:
Department of Informatics, King’s College London, London WC2R 2LS, UK; e-mail: [email protected]; [email protected]
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Abstract

An increase in volumes of data and a shift towards live data enabled a stronger focus on resource-intensive tasks which run continuously over long periods. A Grid has potential to offer the required resources for these tasks, while considering a fair and balanced allocation of resources among multiple client agents. Taking this into account, a Grid might be unwilling to allocate its resources for long time, leading to task interruptions. This problem becomes even more serious if an interruption of one task may lead to the interruption of dependent tasks. Here, we discuss a new strategy for resource re-allocation which is utilized by a client with the aim to prevent too long interruptions by re-allocating resources between its own tasks. Those re-allocations are suggested by a client agent, but only a Grid can re-allocate resources if agreed. Our strategy was tested under the different Grid settings, accounting for the adjusted coefficients, and demonstrated noticeable improvements in client utilities as compared to when it is not considered. Our experiment was also extended to tests with environmental modelling and realistic Grid resource simulation, grounded in real-life Grid studies. These tests have also shown a useful application of our strategy.

Type
EUMAS 15-16
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e9
Copyright
© Cambridge University Press, 2019 

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References

Andrzejak, A. & Ceyran, M. 2005. Characterizing and predicting resource demand by periodicity mining. Network and Systems Management 13(2), 175–196.CrossRefGoogle Scholar
Apache. 2014. Storm - distributed and fault-tolerant realtime computation. http://storm.incubator.apache.org/ Google Scholar
Babu, S. & Widom, J. 2001. Continuous queries over data streams. SIGMOD Record 30(3), 109–120.CrossRefGoogle Scholar
Barbieri, D. F., Braga, D., Ceri, S., Della, Valle, E. & Grossniklaus, M. 2009. C-SPARQL: SPARQL for continuous querying. In The 18th International Conference on World Wide Web. ACM, 1061–1062.Google Scholar
Decker, K. S. & Lesser, V. R. 1992. Generalizing the partial global planning algorithm. International Journal of Intelligent and Cooperative Information Systems 1, 319–346.CrossRefGoogle Scholar
EsperTech. 2014. Event Series Intelligence: Esper & NEsper. http://esper.codehaus.org/ Google Scholar
Foster, I., Kesselman, C. & Tuecke, S. 2001. The anatomy of the grid: Enabling scalable virtual organizations. International Journal of High Performance Computing Applications, 15, 200–222. http://portal.acm.org/citation.cfm?id=1080644.1080667 CrossRefGoogle Scholar
Ghanem, M., Guo, Y., Hassard, J., Osmond, M. & Richards, M. 2004. Sensor Grids for Air Pollution Monitoring. In The 3rd UK e-Science All Hands Meeting.Google Scholar
Haberland, V. 2015. Strategies for the Execution of Long-Term Continuous and Simultaneous Tasks in Grids.PhD thesis NMS. King’s College London.Google Scholar
Haberland, V., Miles, S. and Luck, M. 2014. Negotiation to Execute Continuous Long-Term Tasks. In The 21st European Conference on Artificial Intelligence, Schaub, T. et al. (eds.) Vol. 263. Frontiers in Artificial Intelligence and Applications, 1019–1020.Google Scholar
Haberland, V., Miles, S. & Luck, M. 2015. Adjustable fuzzy inference for adaptive grid resource negotiation. In Next Frontier in Agent-based Complex Automated Negotiation. Vol. 596. Studies of Computational Intelligence. Springer, 37–57.CrossRefGoogle Scholar
Haberland, V., Miles, S. & Luck, M. 2017a. Negotiation strategy for continuous long-term tasks in a grid environment. Autonomous Agents and Multi-Agent Systems 31(1), 130–150.CrossRefGoogle Scholar
Haberland, V., Miles, S. & Luck, M. 2017b. Resource Re-allocation for Data Inter-dependent Continuous Tasks in Grids. In Vol. 10207. LNCS. Springer International Publishing, 187–201.Google Scholar
Iosup, A., Li, H., Jan, M., Anoep, S., Dumitrescu, C., Wolters, L. & Epema, D. H. J. 2008. The grid workloads archive. Future Generation Computer System 24(7), 672–686.CrossRefGoogle Scholar
Jin, H., He, Y., Wen, W. & Liu, H. 2005. A run-time scheduling policy for dependent tasks in grid computing systems. In The 6th International Conference on Parallel and Distributed Computing, Applications and Technologies, 521–523.Google Scholar
Kondo, D., Taufer, M., Brooks, C., Casanova, H. & Chien, A. 2004. Characterizing and evaluating desktop grids: An empirical study. In The 18th International Parallel and Distributed Processing Symposium.Google Scholar
Lacroix, B., Paulus, C. & Mercier, D. 2012. Multi-agent control of thermal systems in buildings. In Proceedings of the 3rd International workshop on Agent Technologies in Energy Systems.Google Scholar
Le-Phuoc, D., Nguyen-Mau, H. Q., Parreira, J. X. & Hauswirth, M. 2012. A middleware framework for scalable management of linked streams. Web Semantics: Science, Services and Agents on the World Wide Web 16(0), 42–51.CrossRefGoogle Scholar
Lee, L.-T., Chen, C.-W., Chang, H.-Y., Tang, C.-C. & Pan, K.-C. 2009. A non-critical path earliest-finish algorithm for inter-dependent tasks in heterogeneous computing environments. In The 11th IEEE International High Performance Computing and Communications, 603–608.Google Scholar
Lesser, V., Decker, K., Wagner, T., Carver, N., Garvey, A., Horling, B., Neiman, D., Podorozhny, R., Nagendra Prasad, M., Raja, A., Vincent, R., Xuan, P. and Zhang, X. Q. 2004. Evolution of the GPGP/TAEMS Domain-Independent Coordination Framework. Autonomous Agents and Multi-Agent Systems 9(1–2), 87–143.CrossRefGoogle Scholar
Lesser, V. R. 1991. A retrospective view of FA/C distributed problem solving. IEEE Transactions on Systems, Man and Cybernetics 21(6), 1347–1362.CrossRefGoogle Scholar
Lim, H. and Babu, S. 2013. Execution and Optimization of Continuous Queries with Cyclops. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, 1069–1072.Google Scholar
Llanes, K. R., Casanova, M. A. & Lemus, N. M. 2016. From Sensor Data Streams to Linked Streaming Data: a survey of main approaches. Journal of Information and Data Management 7(2), 130–140.Google Scholar
Meriem, M. and Belabbas, Y. 2010. Dynamic dependent tasks assignment for grid computing. In Algorithms and Architectures for Parallel Processing, Hsu, C.-H. et al. (eds.) Vol. 6082. LNCS. Springer. 112–120.CrossRefGoogle Scholar
Motwani, R., Widom, J., Arasu, A., Babcock, B., Babu, S., Datar, M., Manku, G., Olston, C., Rosenstein, J. & Varma, R. 2003. Query Processing, Resource Management, and Approximation in a Data Stream Management System. In The 1st Biennial Conference on Innovative Data Systems Research, 245–256.Google Scholar
Sandnes, F. E. and Sinnen, O. 2005. Stochastic DFS for Multiprocessor Scheduling of Cyclic Taskgraphs. In Parallel and Distributed Computing: Applications and Technologies, Liew, K.-M. et al. (eds.) Vol. 3320. LNCS. Springer, 354–362.CrossRefGoogle Scholar
Sardinha, A., Alves, T. A. O., Marzulo, L. A. J., Franca, F. M. G., Barbosa, V. C. & Costa, V. S. 2012. Scheduling cyclic task graphs with SCC-Map. In The 3rd Workshop on Applications for Multi-Core Architectures, 54–59.Google Scholar
Sequeda, J. F. and Corcho, O. 2009. Linked stream data: A position paper. In The 2nd International Workshop on Semantic Sensor Networks. Vol. 522, 148–157.Google Scholar
Terry, D., Goldberg, D., Nichols, D. & Oki, B. 1992. Continuous queries over append-only databases. SIGMOD Rec. 21(2), 321–330.CrossRefGoogle Scholar
TIBCO. 2014. http://www.streambase.com/ Google Scholar
Wooldridge, M. and Jennings, N. R. 1995. Intelligent agents: Theory and practice. The Knowledge Engineering Review 10, 115–152.CrossRefGoogle Scholar
Yan, K. Q., Wang, S. C., Chang, C. P. & Lin, J. S.. 2007. A hybrid load balancing policy underlying grid computing environment. Computer Standards & Interfaces 29(2), 161–173.CrossRefGoogle Scholar
Yang, T. and Fu, C. 1997. Heuristic algorithms for scheduling iterative task computations on distributed memory machines. IEEE Transactions on Parallel and Distributed Systems 8(6), 608–622.CrossRefGoogle Scholar
Zhao, H. & Sakellariou, R. 2004. A low-cost rescheduling policy for dependent tasks on grid computing systems. In The European Across Grids Conference, 21–31.Google Scholar