R&D challenges and emerging solutions for multicore deployment/configuration optimization
Proceedings of the FSE/SDP workshop on Future of software engineering research
ScatterD: Spatial deployment optimization with hybrid heuristic/evolutionary algorithms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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This paper presents the performance analysis of several well-known partitioning scheduling algorithms in real-time and fault-tolerant multiprocessor systems. Both static and dynamic scheduling algorithms are analyzed. Partitioning scheduling algorithms, which are studied here, are heuristic algorithms that are formed by combining any of the bin-packing algorithms with any of the schedulability conditions for the Rate-Monotonic (RM) and Earliest-Deadline-First (EDF) policies. A tool is developed which enables to experimentally evaluate the performance of the algorithms from the graph of tasks. The results show that among several partitioning algorithms evaluated, the RM-Small- Task (RMST) algorithm is the best static algorithm and the EDF-Best-Fit (EDF-BF) is the best dynamic algorithm, for non fault-tolerant systems. For faulttolerant systems which require about 49% more processors, the results show that the RM-First-Fit Decreasing Utilization (RM-FFDU) is the best static algorithm and the EDF-BF is the best dynamic algorithm. To decrease the number of processors in faulttolerant systems, the RMST is modified. The results show that the modified RMST decreases the number of required processors between 7% and 78% in comparison with the original RMST, the RM-FFDU and other well-known static partitioning scheduling algorithms.