Scheduling Analysis Integration for Heterogeneous Multiprocessor SoC
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Efficient Feasibility Analysis for Real-Time Systems with EDF Scheduling
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Battery discharge aware energy feasibility analysis
CODES+ISSS '06 Proceedings of the 4th international conference on Hardware/software codesign and system synthesis
Interactive schedulability analysis
ACM Transactions on Embedded Computing Systems (TECS)
A Constant-Approximate Feasibility Test for Multiprocessor Real-Time Scheduling
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
EDF-schedulability of synchronous periodic task systems is coNP-hard
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Algorithms and complexity for periodic real-time scheduling
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Schedulability analysis of non-preemptive recurring real-time tasks
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Assigning sporadic tasks to unrelated parallel machines
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Algorithms and complexity for periodic real-time scheduling
ACM Transactions on Algorithms (TALG)
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The schedulability analysis problem for many realistic task models is intractable. Therefore known algorithms either have exponential complexity or at best can be solved in pseudo-polynomial time, thereby restricting the application of the concerned models to a large extent. We introduce the notion of "approximate schedulability analysis" and show that if a small amount of "error" (which is specified as an input to the algorithm) can be tolerated in thedecisions made by the algorithm, then this problem can be solved in polynomial time. Our algorithms are analogous to fully polynomial time approximation schemes in the context of optimization problems. We show that this concept of approximate schedulability analysis is fairly general and can be applied to any task model which satisfies certain "task-independence" assumptions. Lastly, we substantiate our theoretical results with experimental evidence and clearly show the tradeoffs between the running time of the schedulability analysis and the error incurred for various values of the input error parameter.