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A Monte Carlo algorithm for real time task scheduling on multi-core processors with software controlled dynamic voltage scaling

dc.contributor.authorMishra, Abhishek
dc.contributor.authorTripathi, Anil Kumar
dc.date.accessioned2020-03-17T06:51:13Z
dc.date.available2020-03-17T06:51:13Z
dc.date.issued2013-10-25
dc.description.abstractThe task scheduling problem for multi-core processors is an important algorithm design issue. Dynamic voltage scaling (DVS) is used to reduce the energy consumption of cores. We ponder the problem of task scheduling on a multi-core processor with software controlled DVS where the objective is to reduce the energy consumption. We consider a system with a single multi-core processor with software controlled DVS having a finite set of core speeds and discuss a task scheduling problem associated with it. The problem that we address is to find a minimum energy task schedule for a given set of independent tasks that have to be completed within a given common deadline. We propose a Monte Carlo algorithm of complexity O(t(mp+q+log(t))+p(t+q)(Dpq+n)) for solving the task scheduling problem and compare it with the optimal algorithm. Here t is the number of tasks, p is the number of cores, q is the number of core speeds, m is an integer parameter that is the number of iterations we should try to get a feasible solution before declaring that no solution is possible, n is an integer parameter that is the number of iterations we should try to reduce the energy consumption when we get a feasible solution, and D is the common deadline of the tasks.en_US
dc.identifier.issn0307904X
dc.identifier.urihttps://idr-sdlib.iitbhu.ac.in/handle/123456789/774
dc.language.isoen_USen_US
dc.publisherElsevier Inc.en_US
dc.subjectDynamic voltage scalingen_US
dc.subjectEnergy efficient schedulingen_US
dc.subjectMulti-core processoren_US
dc.subjectRandomized algorithmen_US
dc.titleA Monte Carlo algorithm for real time task scheduling on multi-core processors with software controlled dynamic voltage scalingen_US
dc.typeArticleen_US

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