Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing

Recent work showed that semi-partitioned scheduling can achieve near-optimal schedulability performance, is simpler to implement compared to global scheduling, and less heavier in terms of runtime overhead, thus resulting in an excellent choice for implementing real-world systems. However, semi-partitioned scheduling typically leverages an off-line design to allocate tasks across the available processors, which requires a-priori knowledge of the workload. Conversely, several simple global schedulers, as global earliest-deadline first (G-EDF), can transparently support dynamic workload without requiring a task-allocation phase. Nonetheless, such schedulers exhibit poor worst-case performance. This work proposes a semi-partitioned approach to efficiently schedule dynamic real-time workload on a multiprocessor system. A linear-time approximation for the C=D splitting scheme under partitioned EDF scheduling is first presented to reduce the complexity of online scheduling decisions. Then, a load-balancing algorithm is proposed for admitting new real-time workload in the system with limited workload re-allocation. A large-scale experimental study shows that the linear-time approximation has a very limited utilization loss compared to the exact technique and the proposed approach achieves very high schedulability performance, with a consistent improvement on G-EDF and pure partitioned EDF scheduling

Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing

Recent work showed that semi-partitioned scheduling can achieve near-optimal schedulability performance, is simpler to implement compared to global scheduling, and less heavier in terms of runtime overhead, thus resulting in an excellent choice for implementing real-world systems. However, semi-partitioned scheduling typically leverages an off-line design to allocate tasks across the available processors, which requires a-priori knowledge of the workload. Conversely, several simple global schedulers, as global earliest-deadline first (G-EDF), can transparently support dynamic workload without requiring a task-allocation phase. Nonetheless, such schedulers exhibit poor worst-case performance. This work proposes a semi-partitioned approach to efficiently schedule dynamic real-time workload on a multiprocessor system. A linear-time approximation for the C=D splitting scheme under partitioned EDF scheduling is first presented to reduce the complexity of online scheduling decisions. Then, a load-balancing algorithm is proposed for admitting new real-time workload in the system with limited workload re-allocation. A large-scale experimental study shows that the linear-time approximation has a very limited utilization loss compared to the exact technique and the proposed approach achieves very high schedulability performance, with a consistent improvement on G-EDF and pure partitioned EDF scheduling

Compensating Adaptive Mixed Criticality Scheduling

The majority of prior academic research into mixed criticality systems assumes that if high-criticality tasks continue to execute beyond the execution time limits at which they would normally finish, then further workload due to low-criticality tasks may be dropped in order to ensure that the high-criticality tasks can still meet their deadlines. Industry, however, takes a different view of the importance of low-criticality tasks, with many practical systems unable to tolerate the abandonment of such tasks. In this paper, we address the challenge of supporting genuinely graceful degradation in mixed criticality systems, thus avoiding the abandonment problem. We explore the Compensating Adaptive Mixed Criticality (C-AMC) scheduling scheme. C-AMC ensures that both high- and low-criticality tasks meet their deadlines in both normal and degraded modes. Under C-AMC, jobs of low-criticality tasks, released in degraded mode, execute imprecise versions that provide essential functionality and outputs of sufficient quality, while also reducing the overall workload. This compensates, at least in part, for the overload due to the abnormal behavior of high-criticality tasks. C-AMC is based on fixed-priority preemptive scheduling and hence provides a viable migration path along which industry can make an evolutionary transition from current practice

Performance Analysis for Segment Stretch Transformation of Parallel Real-time Tasks

International audienceThe Segment Stretch Transformation (SST) is an algorithm that transforms parallel Fork-Join (FJ) tasks into sequential tasks on multiprocessor systems when possible, in order to increase the schedulability of the tasksets of this model. SST is based on Task Stretch Transformation (TST) which is a transformation for the same model of tasks, but it uses segment migrations while SST eliminates their use. In this paper, we prove that SST transformation has the same performance of TST transformation by providing a detailed analysis based on Demand Bound Function (DBF) and by showing that SST has a resource augmentation bound of 3.42, same as TST, which means that if a taskset is feasible on m speed processors, then it is schedulable using the transformation on m processors that are 3.42 times faster

Improving Responsiveness of Time-Sensitive Applications by Exploiting Dynamic Task Dependencies

In this paper, a mechanism is presented for reducing priority inversion in multi-programmed computing systems. Contrarily to well-known approaches from the literature, this paper tackles cases where the dependency relationships among tasks cannot be known in advance to the operating system (OS). The presented mechanism allows tasks to explicitly declare said relationships, enabling the OS scheduler to take advantage of such information and trigger priority inheritance, resulting in reduced priority inversion. We present the prototype implementation of the concept within the Linux kernel, in the form of modifications to the standard POSIX condition variables code, along with an extensive evaluation including a quantitative assessment of the benefits for applications making use of the technique, as well as comprehensive overhead measurements. Also, we present an associated technique for theoretical schedulability analysis of a system using the new mechanism, which is useful to determine whether all tasks can meet their deadlines or not, in the specific scenario of tasks interacting only through remote procedure calls, and under partitioned scheduling

Mixed Criticality on Multi-cores Accounting for Resource Stress and Resource Sensitivity

The most significant trend in real-time systems design in recent years has been the adoption of multi-core processors and the accompanying integration of functionality with different criticality levels onto the same hardware platform. This paper integrates mixed criticality aspects and assurances within a multi-core system model. It bounds cross-core contention and interference by considering the impact on task execution times due to the stress on shared hardware resources caused by co-runners, and each task’s sensitivity to that resource stress. Schedulability analysis is derived for four mixed criticality scheduling schemes based on partitioned fixed priority preemptive scheduling. Each scheme provides robust timing guarantees for high criticality tasks, ensuring that their timing constraints cannot be jeopardized by the behavior or misbehavior of low criticality tasks

Migrating Constant Bandwidth Servers on Multi-Cores

This paper introduces a novel admission test for partitioned CBS reservations on multi-core systems, that, on a new reservation arrival, is capable of exploiting better the CPU capacity in cases in which tasks have just recently left the CPU (for example, due to termination or migration to a different CPU). This is particularly useful for highly dynamic scenarios (with frequent arrivals of new tasks or leaves of existing ones) or when adaptive and possibly power-aware partitioning techniques (in which task migrations are triggered quite often to re-balance the workload among the available cores) are used

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Multicore platforms are being increasingly adopted in Cyber-Physical Systems (CPS) due to their advantages over single-core processors, such as raw computing power and energy efficiency. Typically, multicore platforms use a shared memory bus that connects the cores to the off-chip main memory. This sharing of memory bus may cause tasks running on different cores to compete for access to the main memory whenever data/instructions are need to be read/written from/to the main memory. Such competition is problematic, as it may cause variations in the execution time of tasks in a non-deterministic way. To reduce the complexity of analysing this problem, the 3-phase task model was proposed that divides tasks' executions into distinct memory and execution phases. The distinctive memory phases are then scheduled to eliminate/minimize main memory contention between concurrently executing tasks. However, 3-phase tasks running on different cores may still compete to access the shared memory bus/main memory in order to execute memory phases. This paper presents a partitioned scheduling-based approach that allows one to derive memory bus contention-aware worst-case response time of tasks that follow the 3-phase task model. In particular, the bus-contention analysis is derived by considering two memory access models, i.e., (i) dedicated memory access model, where a core having allowed to access the main memory via memory bus is permitted to execute more than one memory phase, and (ii) fair memory access model, that restrict each core to execute only one memory phase in its allocated bus access. Both these models represent different system and application requirements, and the resulting bus contention of tasks may vary depending on the considered model. To evaluate the effectiveness of the proposed bus contention analysis, we compare its performance against an existing analysis in the state-of-the-art by performing (i) case-study experiments, using benchmarks from the Mälardalen Benchmark suite, and (ii) empirical evaluation using synthetic task sets. Results show that our proposed analysis can improve task set schedulability of 3-phase tasks by up to 88 percentage points.This work was partially supported by European Union’s Horizon 2020 -The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No. 732505. Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER000020” 845 financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement; also by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); by FCT and the Portuguese National Innovation Agency (ANI), under the CMU Portugal partnership, through the European Regional Development Fund (ERDF) of the Operational Competitiveness Programme and Internationaliza850 tion (COMPETE 2020), under the PT2020 Partnership Agreement, within project FLOYD (POCI-01-0247- FEDER-045912), also by FCT under PhD grant 2020.09532.BD.info:eu-repo/semantics/publishedVersio

EDF scheduling of real-time tasks on multiple cores: Adaptive Partitioning vs. Global Scheduling

This paper presents a novel migration algorithm for real-time tasks on multicore systems, based on the idea of migrating tasks only when strictly needed to respect their temporal constraints and a combination of this new algorithm with EDF scheduling. This new “adaptive migration” algorithm is evaluated through an extensive set of simulations showing good performance when compared with global or partitioned EDF: our results highlight that it provides a worst-case utilisation bound similar to partitioned EDF for hard real-time tasks and an empirical tardiness bound (like global EDF) for soft real-time tasks. Therefore, the proposed scheduler is effective for dealing with both hard and soft real-time workloads

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Today multicore processors are used in most modern systems that require computational logic. However, their applicability in systems with stringent timing requirements is still an ongoing research. This is due to the difficulty of ensuring the timing correctness of tasks executing on a multicore platform that comprises a number of shared hardware resources, e.g., caches, memory bus and the main memory. Concurrent accesses to any of these shared resources can generate uncontrolled interference, which complicates the estimations of tasks' worst-case execution time (WCET) and the worst-case response time (WCRT). The use of the 3-phase task execution model helps in upper bounding the contention due to the sharing of bus/main memory in multicore systems. It divides the execution of tasks into distinct memory and execution phases, where tasks can only access the bus/main memory during their memory phases. This makes bus/memory access patterns of tasks more predictable, enabling a preciser computation of bus/memory contention. In this work, we show how the bus contention can be computed for the 3-phase task model considering a work-conserving, i.e., round-robin (RR) based, arbitration policy at the memory bus. This is different from existing works that analyze the time-division multiple access (TDMA) and first-come-first-serve (FCFS) based bus arbitration policies. First, we present a solution to model the bus contention that can be suffered/caused by tasks executing on the same/remote cores of a multicore system under an RR-based bus arbitration scheme. We then evaluate the impact of resulting bus contention on taskset schedulability. Experimental results show that our proposed RR-based bus contention analysis can improve taskset schedulability by up to 100 percentage points than the TDMA-based analysis and up to 40 percentage points than the FCFS-based bus contention analysis.This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB-UIDP/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project POCI-01-0145-FEDER-029119 (PREFECT); also by the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No. 732505. Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER000020” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement; also by FCT, under PhD grant 2020.09532.BD.info:eu-repo/semantics/publishedVersio