Abstract
AbstractWe present a new approximation algorithm for rate-monotonic multiprocessor scheduling of periodic tasks with implicit deadlines. We prove that for an arbitrary parameter k ∈ ℕ it yields solutions with at most \((\frac{3}{2}+\frac{1}{k})OPT+9k\) many processors, thus it gives an asymptotic 3/2-approximation algorithm. This improves over the previously best known ratio of 7/4. Our algorithm can be implemented to run in time O(n 2), where n is the number of tasks. It is based on custom-tailored weights for the tasks such that a greedy maximal matching and subsequent partitioning by a first-fit strategy yields the result.KeywordsSchedule ProblemPeriodic TaskTask VersusMultiprocessor ScheduleRelative DeadlineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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