Integrating Job Parallelism in Real-Time Scheduling Theory

We investigate the global scheduling of sporadic, implicit deadline, real-time task systems on multiprocessor platforms. We provide a task model which integrates job parallelism. We prove that the time-complexity of the feasibility problem of these systems is linear relatively to the number of (sporadic) tasks for a fixed number of processors. We propose a scheduling algorithm theoretically optimal (i.e., preemptions and migrations neglected). Moreover, we provide an exact feasibility utilization bound. Lastly, we propose a technique to limit the number of migrations and preemptions

Theory and Engineering of Scheduling Parallel Jobs

Scheduling is very important for an efficient utilization of modern parallel computing systems. In this thesis, four main research areas for scheduling are investigated: the interplay and distribution of decision makers, the efficient schedule computation, efficient scheduling for the memory hierarchy and energy-efficiency. The main result is a provably fast and efficient scheduling algorithm for malleable jobs. Experiments show the importance and possibilities of scheduling considering the memory hierarchy

Energy-Aware Real-Time Scheduling on Heterogeneous and Homogeneous Platforms in the Era of Parallel Computing

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform\u27s computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time embedded system application\u27s internal parallelism to reduce the worst-case energy consumption (WCEC), an essential requirement for energy-constrained systems. First, we propose an energy-sub-optimal scheduler, assuming the per-core speed tuning feature for each processor. Then we extend our solution to adapt the clustered multi-core platform, where at any given time, all the processors in the same cluster run at the same speed. We also present an analysis to exploit a task\u27s probabilistic information to improve the average-case energy consumption (ACEC), a common non-functional requirement of embedded systems. Due to the strict requirement of temporal correctness, the majority of the real-time system analysis considered the worst-case scenario, leading to resource over-provisioning and cost. The mixed-criticality (MC) framework was proposed to minimize energy consumption and resource over-provisioning. MC scheduling has received considerable attention from the real-time system research community, as it is crucial to designing safety-critical real-time systems. This thesis further addresses energy-aware scheduling of real-time tasks in an MC platform, where tasks with varying criticality levels (i.e., importance) are integrated into a common platform. We propose an algorithm GEDF-VD for scheduling MC tasks with internal parallelism in a multiprocessor platform. We also prove the correctness of GEDF-VD, provide a detailed quantitative evaluation, and reported extensive experimental results. Finally, we present an analysis to exploit a task\u27s probabilistic information at their respective criticality levels. Our proposed approach reduces the average-case energy consumption while satisfying the worst-case timing requirement

Modeling of an Adaptive Parallel System with Malleable Applications in a Distributed Computing Environment

Adaptive parallel applications that can change resources during execution, promise increased application performance and better system utilization. Furthermore, they open the opportunity for developing a new class of parallel applications driven by unpredictable data and events. The research issues in an adaptive parallel system are complex and interrelated. The nature and complexities of the relationships among these issues are not well researched and understood. Before developing adaptive applications or an infrastructure support for adaptive applications, these issues need to be investigated and studied in detail. One way of understanding and investigating these issues is by modeling and simulation. A model for adaptive parallel systems has been developed to enable the investigation of the impact of malleable workloads on its performance. The model can be used to determine how different model parameters impact the performance of the system and to determine the relationships among them Subsequently, a discrete event simulator has been developed to numerically simulate the model. Using the simulator, the impact of the variation in the number of malleable jobs in the workload, the flexibility, the negotiation cost, and the adaptation cost on system performance have been studied. The results and conclusions of these simulation experiments are presented in this dissertation. In general, the simulation results reveal that the performance improves with an increase in the number of malleable jobs in a workload, and that the performance saturates at a certain percentage of rigid to malleable jobs mix. A high percentage of malleable jobs is not necessary to achieve significant improvement in performance. The performance in general improves as the flexibility increases up to a certain point; then, it saturates. The negotiation cost impacts the performance, but not significantly. The number of negotiations for a given workload increases as number of malleable jobs increases up to a certain point, and then it decreases as number of malleable jobs increases further. The performance degrades as the application adaptation cost increases. The impact of the application adaptation cost on performance is much more significant compared to that of the negotiation cost

Modeling of an Adaptive Parallel System with Malleable Applications in a Distributed Computing Environment

Adaptive parallel applications that can change resources during execution, promise increased application performance and better system utilization. Furthermore, they open the opportunity for developing a new class of parallel applications driven by unpredictable data and events. The research issues in an adaptive parallel system are complex and interrelated. The nature and complexities of the relationships among these issues are not well researched and understood. Before developing adaptive applications or an infrastructure support for adaptive applications, these issues need to be investigated and studied in detail. One way of understanding and investigating these issues is by modeling and simulation. A model for adaptive parallel systems has been developed to enable the investigation of the impact of malleable workloads on its performance. The model can be used to determine how different model parameters impact the performance of the system and to determine the relationships among them Subsequently, a discrete event simulator has been developed to numerically simulate the model. Using the simulator, the impact of the variation in the number of malleable jobs in the workload, the flexibility, the negotiation cost, and the adaptation cost on system performance have been studied. The results and conclusions of these simulation experiments are presented in this dissertation. In general, the simulation results reveal that the performance improves with an increase in the number of malleable jobs in a workload, and that the performance saturates at a certain percentage of rigid to malleable jobs mix. A high percentage of malleable jobs is not necessary to achieve significant improvement in performance. The performance in general improves as the flexibility increases up to a certain point; then, it saturates. The negotiation cost impacts the performance, but not significantly. The number of negotiations for a given workload increases as number of malleable jobs increases up to a certain point, and then it decreases as number of malleable jobs increases further. The performance degrades as the application adaptation cost increases. The impact of the application adaptation cost on performance is much more significant compared to that of the negotiation cost