Abstract
This paper describes how the allocation of stream of tasks, with minimum knowledge, is possible in a distributed computing system. In literature, almost all the task allocation models in a distributed computing system require a priori knowledge of tasks execution time, communication time etc. on the processing nodes. Since the task assignment is not known in advance, this time is difficult to estimate. A cluster-based dynamic allocation scheme is proposed for both the distributed computing system and the tasks that eliminate the execution time requirement. Further, as opposed to a single task, multiple tasks are considered for allocation by the model. For both the task clustering and processor clustering a fuzzy function is used. Clustering and assignment process is used dynamically as it suits the stochastic stream of incoming tasks. Experimental validate the efficacy of the proposed model.
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Vidyarthi, D.P., Tripathi, A.K., Sarker, B.K., Yang, L.T. (2005). Dynamic Clustering of Tasks and DCS for Multiple Task Allocation. In: Guo, M., Yang, L.T. (eds) New Horizons of Parallel and Distributed Computing. Springer, Boston, MA. https://doi.org/10.1007/0-387-28967-4_9
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DOI: https://doi.org/10.1007/0-387-28967-4_9
Publisher Name: Springer, Boston, MA
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