Reducing Tardiness Under Global Scheduling by Splitting Jobs ∗

Under current analysis, soft real-time tardiness bounds applicable to global earliest-deadline-first scheduling and related policies depend on per-task worst-case execution times. By splitting job budgets to create subjobs with shorter periods and worst-case execution times, such bounds can be reduced to near zero for implicit-deadline sporadic task systems. However, doing so could potentially cause more preemptions and create problems for synchronization protocols. This paper analyzes this tradeoff between theory and practice by presenting an overhead-aware schedulability study pertaining to job splitting. In this study, real overhead data from a scheduler implementation in LITMUSRT was factored into schedulability analysis. This study shows that despite practical issues affecting job splitting, it can still yield substantial reductions in tardiness bounds for soft real-time systems.

SCHEDULING REAL-TIME GRAPH-BASED WORKLOADS

Developments in the semiconductor industry in the previous decades have made possible computing platforms with very large computing capacities that, in turn, have stimulated the rapid progress of computationally intensive computer vision (CV) algorithms with highly parallelizable structure (often represented as graphs). Applications using such algorithms are the foundation for the transformation of semi-autonomous systems (e.g., advanced driver-assist systems) to future fully-autonomous systems (e.g., self-driving cars). Enabling mass-produced safety-critical systems with full autonomy requires real-time execution guarantees as a part of system certification.Since multiple CV applications may need to share the same hardware platform due to size, weight, power, and cost constraints, system component isolation is necessary to avoid explosive interference growth that breaks all execution guarantees. Existing software certification processes achieve component isolation through time partitioning, which can be broken by accelerator usage, which is essential for high-efficacy CV algorithms.The goal of this dissertation is to make a first step towards providing real-time guarantees for safety-critical systems by analyzing the scheduling of highly parallel accelerator-using workloads isolated in system components. The specific contributions are threefold.First, a general method for graph-based workloads’ response-time-bound reduction through graph structure modifications is introduced, leading to significant response-time-bound reductions. Second, a generalized real-time task model is introduced that enables real-time response-time bounds for a wider range of graph-based workloads. A proposed response-time analysis for the introduced model accounts for potential accelerator usage within tasks. Third, a scheduling approach for graph-based workloads in a single system component is proposed that ensures the temporal isolation of system components. A response-time analysis for workloads with accelerator usage is presented alongside a non-mandatory schedulability-improvement step. This approach can help to enable component-wise certification in the considered systems.Doctor of Philosoph

COMBINING HARDWARE MANAGEMENT WITH MIXED-CRITICALITY PROVISIONING IN MULTICORE REAL-TIME SYSTEMS

Safety-critical applications in cyber-physical domains such as avionics and automotive systems require strict timing constraints because loss of life or severe financial repercussions may occur if they fail to produce correct outputs at the right moment. We call such systems “real-time systems.” When designing a real-time system, a multicore platform would be desirable to use because such platforms have advantages in size, weight, and power constraints, especially in embedded systems. However, the multicore revolution is having limited impact in safety-critical application domains. A key reason is the “one-out-of-m” problem: when validating real-time constraints on an m-core platform, excessive analysis pessimism can effectively negate the processing capacity of the additional m-1 cores so that only “one core’s worth” of capacity is available. The root of this problem is that shared hardware resources are not predictably managed. Two approaches have been investigated previously to address this problem: mixed-criticality analysis, which provision less-critical software components less pessimistically, and hardware-management techniques, which make the underlying platform itself more predictable. The goal of the research presented in this dissertation is to combine both approaches to reduce the capacity loss caused by contention for shared hardware resources in multicore platforms. Towards that goal, fundamentally new criticality-cognizant hardware-management tradeoffs must be explored. Such tradeoffs are investigated in the context of a new variant of a mixed-criticality framework, called MC2, that supports configurable criticality-based hardware management. This framework allows specific DRAM banks and areas of the last-level cache to be allocated to certain groups of tasks to provide criticality-aware isolation. MC2 is further extended to support the sharing of memory locations, which is required to realize the ability to support real-world workloads. We evaluate the impact of combining mixed-criticality provisioning and hardware-management techniques with both micro-benchmark experiments and schedulability studies. In our micro-benchmark experiments, we evaluate each hardware-management technique and consider tradeoffs that arise when applying them together. The effectiveness of the overall framework in resolving such tradeoffs is investigated via largescale overhead-aware schedulability studies. Our results demonstrate that mixed-criticality analysis and hardware-management techniques can be much more effective when applied together instead of alone.Doctor of Philosoph

Tardiness Bounds and Overload in Soft Real-Time Systems

In some systems, such as future generations of unmanned aerial vehicles (UAVs), different software running on the same machine will require different timing guarantees. For example, flight control software has hard real-time (HRT) requirements---if a job (i.e., invocation of a program) completes late, then safety may be compromised, so jobs must be guaranteed to complete within short deadlines. However, mission control software is likely to have soft real-time (SRT) requirements---if a job completes slightly late, the result is not likely to be catastrophic, but lateness should never be unbounded. The global earliest-deadline-first (G-EDF) scheduler has been demonstrated to be useful for the multiprocessor scheduling of software with SRT requirements, and the multicore mixed-criticality (MC2) framework using G-EDF for SRT scheduling has been proposed to safely mix HRT and SRT work on multicore UAV platforms. This dissertation addresses limitations of this prior work. G-EDF is attractive for SRT systems because it allows the system to be fully utilized with reasonable overheads. Furthermore, previous analysis of G-EDF can provide "lateness bounds" on the amount of time between a job's deadline and its completion. However, smaller lateness bounds are preferable, and some programs may be more sensitive to lateness than others. In this dissertation, we explore the broader category of G-EDF-like (GEL) schedulers that have identical overhead characteristics to G-EDF. We show that by choosing GEL schedulers other than G-EDF, better lateness can be achieved, and that certain modifications can further improve lateness bounds while maintaining reasonable overheads. Specifically, successive jobs from the same program can be permitted to run in parallel with each other, or jobs can be split into smaller pieces by the operating system. Previous analysis of MC2 has always used less pessimistic execution time assumptions when analyzing SRT work than when analyzing HRT work. These assumptions can be violated, creating an overload that causes SRT guarantees to be violated. Furthermore, even in the expected case that such violations are transient, the system is not guaranteed to return to its normal operation. In this dissertation, we also provide a mechanism that can be used to provide such recovery.Doctor of Philosoph