Complex scheduling models and analyses for property-based real-time embedded systems

Modern multi core architectures and parallel applications pose a significant challenge to the worst-case centric real-time system verification and design efforts. The involved model and parameter uncertainty contest the fidelity of formal real-time analyses, which are mostly based on exact model assumptions. In this dissertation, various approaches that can accept parameter and model uncertainty are presented. In an attempt to improve predictability in worst-case centric analyses, the exploration of timing predictable protocols are examined for parallel task scheduling on multiprocessors and network-on-chip arbitration. A novel scheduling algorithm, called stationary rigid gang scheduling, for gang tasks on multiprocessors is proposed. In regard to fixed-priority wormhole-switched network-on-chips, a more restrictive family of transmission protocols called simultaneous progression switching protocols is proposed with predictability enhancing properties. Moreover, hierarchical scheduling for parallel DAG tasks under parameter uncertainty is studied to achieve temporal- and spatial isolation. Fault-tolerance as a supplementary reliability aspect of real-time systems is examined, in spite of dynamic external causes of fault. Using various job variants, which trade off increased execution time demand with increased error protection, a state-based policy selection strategy is proposed, which provably assures an acceptable quality-of-service (QoS). Lastly, the temporal misalignment of sensor data in sensor fusion applications in cyber-physical systems is examined. A modular analysis based on minimal properties to obtain an upper-bound for the maximal sensor data time-stamp difference is proposed

Hard Real-Time Stationary GANG-Scheduling

The scheduling of parallel real-time tasks enables the efficient utilization of modern multiprocessor platforms for systems with real-time constrains. In this situation, the gang task model, in which each parallel sub-job has to be executed simultaneously, has shown significant performance benefits due to reduced context switches and more efficient intra-task synchronization. In this paper, we provide the first schedulability analysis for sporadic constrained-deadline gang task systems and propose a novel stationary gang scheduling algorithm. We show that the schedulability problem of gang task sets can be reduced to the uniprocessor self-suspension schedulability problem. Furthermore, we provide a class of partitioning algorithms to find a stationary gang assignment and show that it bounds the worst-case interference of each task. To demonstrate the effectiveness of our proposed approach, we evaluate it for implicit-deadline systems using randomized task sets under different settings, showing that our approach outperforms the state-of-the-art

Response-Time Analysis for Non-Preemptive Periodic Moldable Gang Tasks

Gang scheduling has long been adopted by the high-performance computing community as a way to reduce the synchronization overhead between related threads. It allows for several threads to execute in lock steps without suffering from long busy-wait periods or be penalized by large context-switch overheads. When combined with non-preemptive execution, gang scheduling significantly reduces the execution time of threads that work on the same data by decreasing the number of memory transactions required to load or store the data. In this work, we focus on two main types of gang tasks: rigid and moldable. A moldable gang task has a presumed known minimum and maximum number of cores on which it can be executed at runtime, while a rigid gang task always executes on the same number of cores. This work presents the first response-time analysis for non-preemptive moldable gang tasks. Our analysis is based on the notion of schedule abstraction; a new approach for response-time analysis with the promise of high accuracy. Our experiments on periodic rigid gang tasks show that our analysis is 4.9 times more successful in identifying schedulable tasks than the existing utilization-based test for rigid gang tasks.</p

Scheduling Real-Time Jobs in Distributed Systems - Simulation and Performance Analysis

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.One of the major challenges in ultrascale systems is the effective scheduling of complex jobs within strict timing constraints. The distributed and heterogeneous system resources constitute another critical issue that must be addressed by the employed scheduling strategy. In this paper, we investigate by simulation the performance of various policies for the scheduling of real-time directed acyclic graphs in a heterogeneous distributed environment. We apply bin packing techniques during the processor selection phase of the scheduling process, in order to utilize schedule gaps and thus enhance existing list scheduling methods. The simulation results show that the proposed policies outperform all of the other examined algorithms.The work presented in this paper has been partially supported by EU under the COST program Action IC1305, “Network for Sustainable Ultrascale Computing (NESUS)”

Budgeting Under-Specified Tasks for Weakly-Hard Real-Time Systems

In this paper, we present an extension of slack analysis for budgeting in the design of weakly-hard real-time systems. During design, it often happens that some parts of a task set are fully specified while other parameters, e.g. regarding recovery or monitoring tasks, will be available only much later. In such cases, slack analysis can help anticipate how these missing parameters can influence the behavior of the whole system so that a resource budget can be allocated to them. It is, however, sufficient in many application contexts to budget these tasks in order to preserve weakly-hard rather than hard guarantees. We thus present an extension of slack analysis for deriving task budgets for systems with hard and weakly-hard requirements. This work is motivated by and validated on a realistic case study inspired by industrial practice

3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems

Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity

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