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Abstract: In this paper, we focus on the semi-partitioned scheduling of sporadic tasks with constrained deadlines and identical processors. We study two cases of semi-partitioning: (i) the case where the worst case execution time (WCET) of a job can be portioned, each portion being executed on a dedicated processor, according to a static pattern of migration; (ii) the case where the jobs of a task are released on a processor, 1 time out of p, where p is an integer at most equal to the number of processors, according to a round-robin migration pattern. The first approach has been investigated in the state-of-the-art by migrating a job at its local deadline, computed from the deadline of the task it belongs to. We study several local deadline assignment heuristics (fair, based on processor utilization and based on the minimum acceptable local deadline for a job on a processor). In both cases, we propose feasibility conditions for the schedulability of sporadic tasks scheduled using earliest deadline first (EDF) semi-partitioned scheduling. We show that the load function used for global scheduling to establish the feasibility of sporadic task sets exhibits interesting properties in the semi-partitioning context. We carry out simulations to study the performance of the two approaches in terms of success rate and number of migrations, for platforms composed of four and eight processors. We compare the performance of these semi-partitioned heuristics with the performance of classical partitioned scheduling algorithms and with a global scheduling heuristic which is currently considered to have good performances. [Copyright &y& Elsevier]

Abstract: This paper explores scheduling a realistic variant of open shops with parallel machines per working stage. Since real production floors seldom employ a single machine for each operation, the regular open shop problem is very often in practice extended with a set of parallel machines at each stage. The purpose of duplicating machines in parallel is to either eliminate or to reduce the impact of bottleneck stages on the overall shop efficiency. The objective is to find the sequence which minimizes total completion times of jobs. We first formulate the problem as an effective mixed integer linear programming model, and then we employ memetic algorithms to solve the problem. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of memetic algorithm. To further enhance the memetic algorithm, we hybridize it with a simple form of simulated annealing as its local search engine. To assess the performance of the model and algorithms, we establish two computational experiments. The first one is small-sized instances by which the model and general performance of the algorithms are evaluated. The second one consists of large-sized instances by which we further evaluate the algorithms. [ABSTRACT FROM AUTHOR]

Abstract: The most crucial aspect of distributed real-time systems is the scheduling algorithm, which must guarantee that every job in the system will meet its deadline. In this paper, we evaluate by simulation the performance of strategies for the dynamic scheduling of composite jobs in a heterogeneous distributed real-time system. Each job that arrives in the system is a directed acyclic graph of component tasks and has an end-to-end deadline. For each scheduling policy, we provide alternative versions which allow the insertion of tasks into idle time slots, using various bin packing techniques. The comparison study, based on different workloads and system heterogeneity levels, shows that the alternative versions of the algorithms outperform their respective counterparts. [ABSTRACT FROM AUTHOR]