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RTAS '04 Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium
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Sporadic Multiprocessor Scheduling with Few Preemptions
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ACM Computing Surveys (CSUR)
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Resource Augmentation Bounds for Approximate Demand Bound Functions
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Execution migration in a heterogeneous-ISA chip multiprocessor
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
The Power of Preemption on Unrelated Machines and Applications to Scheduling Orders
Mathematics of Operations Research
Outstanding Paper Award: Task Assignment Algorithms for Two-Type Heterogeneous Multiprocessors
ECRTS '12 Proceedings of the 2012 24th Euromicro Conference on Real-Time Systems
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Consider the problem of assigning implicit-deadline sporadic tasks on a heterogeneous multiprocessor platform comprising two different types of processors--such a platform is referred to as two-type platform. We present two low degree polynomial time-complexity algorithms, SA and SA-P, each providing the following guarantee. For a given two-type platform and a task set, if there exists a task assignment such that tasks can be scheduled to meet deadlines by allowing them to migrate only between processors of the same type (intra-migrative), then (i) using SA, it is guaranteed to find such an assignment where the same restriction on task migration applies but given a platform in which processors are $1+\frac{\alpha}{2}$ times faster and (ii) SA-P succeeds in finding a task assignment where tasks are not allowed to migrate between processors (non-migrative) but given a platform in which processors are 1+驴 times faster. The parameter 0驴≤1 is a property of the task set; it is the maximum of all the task utilizations that are no greater than 1.We evaluate average-case performance of both the algorithms by generating task sets randomly and measuring how much faster processors the algorithms need (which is upper bounded by $1+\frac{\alpha}{2}$ for SA and 1+驴 for SA-P) in order to output a feasible task assignment (intra-migrative for SA and non-migrative for SA-P). In our evaluations, for the vast majority of task sets, these algorithms require significantly smaller processor speedup than indicated by their theoretical bounds.Finally, we consider a special case where no task utilization in the given task set can exceed one and for this case, we (re-)prove the performance guarantees of SA and SA-P.We show, for both of the algorithms, that changing the adversary from intra-migrative to a more powerful one, namely fully-migrative, in which tasks can migrate between processors of any type, does not deteriorate the performance guarantees. For this special case, we compare the average-case performance of SA-P and a state-of-the-art algorithm by generating task sets randomly. In our evaluations, SA-P outperforms the state-of-the-art by requiring much smaller processor speedup and by running orders of magnitude faster.