Parameter adaptation for generalized multiframe tasks: schedulability analysis, case study, and applications to self-suspending tasks

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods may change to alleviate temporal overload, for example in the parameter adaption and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaption). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming (MILP) is given to check the schedulability under EDF scheduling and optimally assign deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when task parameters must be integers. An approximation algorithm is also deployed to reduce computational running time. The speed-up factor of our approximation algorithm is 1+a#x0395; where a#x0395; can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done exhaustive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.

End-to-end flows, which have a set of chainlike subtasks, are widely used in distributed real-time systems. For instance, multimedia and automative applications require that subtasks finish executing on a chain of processors before their end-to-end deadlines. The scheduling of such chained subtasks decides the schedulability of a distributed realtime system. Since the subtask priority assignment problem is NP-hard in general, most heuristics are presented to schedule end-to-end flows in two separate steps. The first step calculates intermediate relative deadlines for frames, and the second step makes scheduling decisions under EDF scheduling. Because the quality of the priority assignment of subtasks will directly affect the schedulability of the distributed systems, the two separate steps may cause pessimism in schedulability analysis. To reduce potential pessimism, we combine the two steps in our novel dGMF-PA (distributed generalized multiframe tasks with parameter adaption) model. We present an algorithm based on mixed-integer linear programming for optimally selecting frame relative deadlines in the dGMF-PA model. An approximation algorithm is also proposed to reduce computational running time. Our approximation algorithm has a tunable speed-up factor of 1 + ϵ where ϵ can be arbitrarily small, with respect to the exact schedulability test of dGMF-PA tasks under EDF scheduling. Extensive experiments have shown that our approximation algorithm (which is a sufficient schedulability test) can schedule at most 44 % more than HOSPA, an existing state-of-the-art algorithm.

The integration of mixed-critical tasks into a platform is an increasingly important trend in the design of real-time systems due to its efficient resource usage. With a growing variety of activation patterns considered in real-time systems, some of them capture arbitrary activation patterns. As a consequence, the existing scheduling approaches in mixed-criticality systems (MCs), which assume the sporadic tasks with implicit deadlines, have sometimes become inapplicable or are ineffective. In this paper, we extend the sporadically activated task model to the arbitrarily activated task model in MCs with the preemptive fixed-task-priority schedule. By using the event arrival curve to model task activations, we present the necessary and sufficient schedulability tests that are based on the well-established results from Real-Time Calculus. We propose to use the busy-window analysis to do the sufficient test because it has been shown to be tighter than the sufficient test of using Real-Time Calculus. According to our experimental results, for sporadic task sets, our proposed test can achieve the same performance as the state-of-the-art schedulability test. However, compared with the previous schedulability analysis of preemptive fixed-task-priority, our approaches can handle more general tasks with blocking, jitter, and arbitrary deadlines.

The multiprocessor EDF scheduling of sporadic task systems is studied. A new sufficient schedulability test is presented and proved correct. It is shown that this test generalizes the previously-kn...

In this paper we introduce a robust earliest deadline scheduling algorithm for dealing with hard aperiodic tasks under overloads in a dynamic real-time environment. The algorithm synergistically combines many features including dynamic guarantees, graceful degradation in overloads, deadline tolerance, resource reclaiming, and dynamic re-guarantees. A necessary and sufficient schedulability test is presented, and an efficient 0(n) guarantee algorithm is proposed. The new algorithm is evaluated via simulation and compared to several baseline algorithms. The experimental results show excellent performance of the new algorithm in normal and overload conditions.

Consider constrained-deadline sporadic tasks scheduled on a multiprocessor where (i) each task is characterized by its execution requirement, deadline, and minimum inter-arrival time, (ii) each task generates a sequence of jobs, (iii) the execution requirement of a job and its potential for parallel execution is described by one or many stages with a stage having one or many segments and different segments in the same stage can execute in parallel and a segment is only allowed to start execution if all segments of previous stages have finished execution, and (iv) there is contention for shared resources in the memory system (cache eviction, reordering in memory controller, memory bus contention). We present an algorithm that (i) performs schedulability testing for tasks scheduled with global-Earliest-Deadline-First (gEDF), (ii) configures the virtual-to-physical address translation so that a cache block fetched to the last-level cache by one task cannot be evicted by another task, (iii) configures the virtual-to-physical address translation to attempt to eliminate the extra execution time caused by the reordering effect in the memory controller and if this is not possible, then the reordering effect is considered in the schedulability analysis, and (iv) considers the effect of contention for the memory bus. Our solution is based on formulating this problem as a Mixed-Integer Linear Program (MILP). We have implemented a tool based on this theory and validated its output against measurements on a real computer.

As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF(k). We prove that the union of the EDZL schedulability tests dominates the EDF(k) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF(k) schedulability test. We also compare the schedulability of EDZL and EDF(k) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF(k).

Test scheduling for core-based SoCs is a challenging problem. Test schedules must be crafted with the objectives of minimizing testing time and ATE vector memory requirements, to reduce test cost, under the constraints of total available test access mechanism (TAM) width. Prior research in test scheduling has mainly used search procedures like ILP and rectangle packing to solve this problem, but these approaches are inherently computationally expensive. In this paper we describe a novel algorithm to solve the test scheduling problem using a combination of network flow algorithms, malleable job scheduling and reconfigurable wrapper design. Our approximation algorithm has polynomial time complexity and produces schedules close to the theoretical lower bound. Extensive experimental results using the new ITC'02 SoC benchmarks validate the quality of our solutions.

In real-time scheduling theory, self-suspension describes the behavior that a job can suspend itself from the ready state and thus be exempted from the scheduling for the suspension duration. This behavior makes it non-trivial to resort to established concepts such as the busy-interval analysis to self-suspending task sets which is required to analyze the worst-case response time of tasks with backlog, e.g., arbitrary-deadline task sets. In this paper, we present a novel suspension-aware busy-interval analysis for dynamic self-suspension tasks where the inter-arrival time of subsequent jobs can be bounded by an arrival curve. Based on the general analysis, we provide worst-case response time analyses and hence sufficient schedulability tests for fixed-priority preemptive uniprocessor scheduling algorithms for arrival-curve constrained and sporadic self-suspension task systems with arbitrary deadlines. Moreover, we provide evaluations based on synthetically generated task sets that show that our method indeed exploits the optimism that is introduced when enlarging the relative deadline of tasks. We demonstrate that our approach improves the state of the art by considering arrival curves that are obtained from tasks with release jitter.

We address the problem of schedulability analysis for a set of sporadic real-time tasks scheduled by the Global Earliest Deadline First (G-EDF) policy on a multiprocessor platform. State-of-the-art tests for schedulability analysis of multiprocessor global scheduling are often incomparable. That is, a task set that is judged not schedulable by a test may be verified to be schedulable by another test, and vice versa. In this paper, we first develop a new schedulability test that integrates the limited carry-in technique and Response Time Analysis (RTA) procedure for Global EDF schedulability analysis. Then, we provide an over-approximate variant of this test with better run-time efficiency. Later, we extend these two tests to self-suspending tasks. All schedulability tests proposed in the paper have provable dominance over their state-of-the-art counterparts. Finally, we conduct extensive comparisons among different schedulability tests. Our new tests show significant improvements for schedulability analysis of Global EDF.

Robust Adaptive Control

On the scheduling of leak detection personnel

Multicore processors are increasingly adopted in resource-constrained real-time embedded applications. In the development of such applications, efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and to reduce system stack usage. In this paper, we consider partitioned multi-processor scheduling on a multicore processor with either Fixed-Priority or Earliest Deadline First scheduling algorithms with PTS and address the design optimization problem of mapping tasks to processor cores and assignment of task priorities and preemption thresholds with the optimization objective of minimizing system stack usage. We present both optimal solution techniques based on Mixed Integer Linear Programming and efficient heuristic algorithms that can achieve high-quality results. We perform extensive performance evaluations using both synthetic tasksets and industrial case studies.

Increasingly, real-time systems must handle the self-suspension of tasks (that is, lower-bound wait times between subtasks) in a timely and predictable manner. A fast schedulability test that does not significantly overestimate the temporal resources needed to execute self-suspending task sets would be of benefit to these modern computing systems. In this paper, a polynomial-time test is presented that is known to be the first to handle nonpreemptive self-suspending task sets with hard deadlines, where each task has any number of self-suspensions. To construct the test, a novel priority scheduling policy is leveraged, the th subtask first, which restricts the behavior of the self-suspending model to provide an analytical basis for an informative schedulability test. In general, the problem of sequencing according to both upper-bound and lower-bound temporal constraints requires an idling scheduling policy and is known to be nondeterministic polynomial-time hard. However, the tightness of the schedulability test and scheduling algorithm are empirically validated, and it is shown that the processor is able to effectively use up to 95% of the self-suspension time to execute tasks.

A model is considered for representing recurrent precedence-constrained tasks that are to execute on multiprocessor platforms. A recurrent task is specified as a directed acyclic graph (DAG), a period, and a relative deadline. Each vertex of the DAG represents a sequential job, while the edges of the DAG represent precedence constraints between these jobs. All the jobs of the DAG are released simultaneously and need to complete execution within the specified relative deadline of their release. Each task may release jobs in this manner an unbounded number of times, with successive releases occurring at least the specified period apart. Conditional control structures are also allowed. The scheduling problem is to determine whether a set of such recurrent tasks can be scheduled to always meet all deadlines upon a specified number of identical processors. This problem is shown to be computationally intractable, but amenable to efficient approximate solutions. Earliest Deadline First (EDF) and Deadline Monotonic (DM) are shown to be good approximate global scheduling algorithms. Polynomial and pseudo-polynomial time schedulability tests, of differing effectiveness, are presented for determining whether a given task set can be scheduled by EDF or DM to always meet deadlines on a specified number of processors.