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1. Optimal soft real-time semi-partitioned scheduling made simple (and dynamic)

   https://doi.org/10.1145/3356401.3356402  

   Hobbs, Clara; Tong, Zelin; Anderson, James H. (November 2019, Proceedings of the 27th International Conference on Real-Time Networks and Systems)

   Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system. 

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2. An Optimal Semi-Partitioned Scheduler Assuming Arbitrary Affinity Masks

   https://doi.org/10.1109/RTSS.2018.00055  

   Voronov, Sergey; Anderson, James H. (December 2018, 2018 IEEE Real-Time Systems Symposium (RTSS))

   Modern operating systems allow task migrations to be restricted by specifying per-task processor affinity masks. Such a mask specifies the set of processor cores upon which a task can be scheduled. In this paper, a semi-partitioned scheduler, AM-Red (affinity mask reduction), is presented for scheduling implicit-deadline sporadic tasks with arbitrary affinity masks on an identical multiprocessor. AM-Red is obtained by applying an affinity-mask-reduction method that produces affinities in accordance with those specified, without compromising feasibility, but with only a linear number of migrating tasks. It functions by employing a tunable frame of size |F|. For any choice of |F|, AM-Red is soft-real-time optimal, with tardiness bounded by |F|, but the frequency of task migrations is proportional to |F|. If |F| divides all task periods, then AM-Red is also hard-real-time-optimal (tardiness is zero). AM-Red is the first optimal scheduler proposed for arbitrary affinity masks without future knowledge of all job releases. Experiments are presented that show that AM-Red is implementable with low overhead and yields reasonable tardiness and task-migration frequency. 

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3. Efficient Deterministic Federated Scheduling for Parallel Real-Time Tasks

   https://doi.org/10.1109/RTCSA50079.2020.9203660  

   Dinh, Son; Gill, Christopher; Agrawal, Kunal (August 2020, 2020 IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA))

   null (Ed.)

   Federated scheduling is a generalization of partitioned scheduling for parallel tasks on multiprocessors, and has been shown to be a competitive scheduling approach. However, federated scheduling may waste resources due to its dedicated allocation of processors to parallel tasks. In this work we introduce a novel algorithm for scheduling parallel tasks that require more than one processor to meet their deadlines (i.e., heavy tasks). The proposed algorithm computes a deterministic schedule for each heavy task based on its internal graph structure. It efficiently exploits the processors allocated to each task and thus reduces the number of processors required by the task. Experimental evaluation shows that our new federated scheduling algorithm significantly outperforms other state-of-the-art federated-based scheduling approaches, including semi-federated scheduling and reservation-based federated scheduling, that were developed to tackle resource waste in federated scheduling, and a stretching algorithm that also uses the tasks' graph structures. 

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4. A Soft-Real-Time-Optimal Semi-Clustered Scheduler with a Constant Tardiness Bound

   https://doi.org/10.1109/RTCSA50079.2020.9203605  

   Ahmed, Shareef; Anderson, James H. (August 2020, Proceedings of the 26th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications)

   null (Ed.)

   Different global and semi-partitioned schedulers have been proposed that are soft-real-time (SRT) optimal for sporadic task systems, meaning they can guarantee bounded deadline tardiness. However, under known analyses, tardiness bounds increase with respect to the number of processors, which reduces the applicability of these schedulers in systems with a large number of processors. In this paper, a semi-clustered scheduler, SC-EDF, is presented that has a constant tardiness bound. SC-EDF partitions tasks into clusters, each of which may include one fractional processor. Each cluster is scheduled by G-EDF, and the fractional processors are realized using Pfair scheduling techniques. 

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5. Partitioned hybrid learning of Bayesian network structures

   https://doi.org/10.1007/s10994-022-06145-4  

   Huang, Jireh; Zhou, Qing (March 2022, Machine Learning)

   Abstract We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: Partitioned PC (pPC) accelerates skeleton learning via a divide-and-conquer strategy,p-value adjacency thresholding (PATH) effectively accomplishes parameter tuning with a single execution, and hybrid greedy initialization (HGI) maximally utilizes constraint-based information to obtain a high-scoring and well-performing initial graph for greedy search. We establish structure learning consistency of our algorithms in the large-sample limit, and empirically validate our methods individually and collectively through extensive numerical comparisons. The combined merits of pPC and PATH achieve significant computational reductions compared to the PC algorithm without sacrificing the accuracy of estimated structures, and our generally applicable HGI strategy reliably improves the estimation structural accuracy of popular hybrid algorithms with negligible additional computational expense. Our empirical results demonstrate the competitive empirical performance of pHGS against many state-of-the-art structure learning algorithms. 

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