Statistic Rate Monotonic Scheduling

In this paper we present Statistical Rate Monotonic Scheduling (SRMS), a generalization of the classical RMS results of Liu and Layland that allows scheduling periodic tasks with highly variable execution times and statistical QoS requirements. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. The feasibility test for SRMS ensures that using SRMS' scheduling algorithms, it is possible for a given periodic task set to share a given resource (e.g. a processor, communication medium, switching device, etc.) in such a way that such sharing does not result in the violation of any of the periodic tasks QoS constraints. The SRMS scheduling algorithm incorporates a number of unique features. First, it allows for fixed priority scheduling that keeps the tasks' value (or importance) independent of their periods. Second, it allows for job admission control, which allows the rejection of jobs that are not guaranteed to finish by their deadlines as soon as they are released, thus enabling the system to take necessary compensating actions. Also, admission control allows the preservation of resources since no time is spent on jobs that will miss their deadlines anyway. Third, SRMS integrates reservation-based and best-effort resource scheduling seamlessly. Reservation-based scheduling ensures the delivery of the minimal requested QoS; best-effort scheduling ensures that unused, reserved bandwidth is not wasted, but rather used to improve QoS further. Fourth, SRMS allows a system to deal gracefully with overload conditions by ensuring a fair deterioration in QoS across all tasks---as opposed to penalizing tasks with longer periods, for example. Finally, SRMS has the added advantage that its schedulability test is simple and its scheduling algorithm has a constant overhead in the sense that the complexity of the scheduler is not dependent on the number of the tasks in the system. We have evaluated SRMS against a number of alternative scheduling algorithms suggested in the literature (e.g. RMS and slack stealing), as well as refinements thereof, which we describe in this paper. Consistently throughout our experiments, SRMS provided the best performance. In addition, to evaluate the optimality of SRMS, we have compared it to an inefficient, yet optimal scheduler for task sets with harmonic periods.National Science Foundation (CCR-970668

Mixed-Criticality Scheduling with I/O

This paper addresses the problem of scheduling tasks with different criticality levels in the presence of I/O requests. In mixed-criticality scheduling, higher criticality tasks are given precedence over those of lower criticality when it is impossible to guarantee the schedulability of all tasks. While mixed-criticality scheduling has gained attention in recent years, most approaches typically assume a periodic task model. This assumption does not always hold in practice, especially for real-time and embedded systems that perform I/O. For example, many tasks block on I/O requests until devices signal their completion via interrupts; both the arrival of interrupts and the waking of blocked tasks can be aperiodic. In our prior work, we developed a scheduling technique in the Quest real-time operating system, which integrates the time-budgeted management of I/O operations with Sporadic Server scheduling of tasks. This paper extends our previous scheduling approach with support for mixed-criticality tasks and I/O requests on the same processing core. Results show the effective schedulability of different task sets in the presence of I/O requests is superior in our approach compared to traditional methods that manage I/O using techniques such as Sporadic Servers.Comment: Second version has replaced simulation experiments with real machine experiments, third version fixed minor error in Equation 5 (missing a plus sign

On Scheduling Real-Time Periodic Tasks in a Multiprocessor Environment.

Real-Time periodic tasks are at the heart of many critical processing computer systems. Nuclear power plants, military command and control systems, aircraft automatic flight control systems, hospital life-support equipment all require precise processing performed within very strict timelines. Missing deadlines can have catastrophic consequences. The rate-monotonic priority assignment policy for scheduling hard-deadline periodic tasks was developed to guarantee those deadlines. In this thesis, we study the problem of scheduling hard-deadline periodic tasks. We begin by surveying the current state of multiprocessor rate-monotonic scheduling and reviewing earlier work. We present the results of a number of experiments we conducted to evaluate the performance of several scheduling heuristics. These heuristics assumed a homogeneous multiprocessing environment. We relax that restriction and introduce three allocation heuristics for scheduling tasks on heterogeneous multiprocessors. Furthermore, we analyze the performance of the proposed algorithms. We compare the quality of the solutions produced by these algorithms and measure them against the optimal solution. Lacking in the current set of real-time multiprocessor heuristics is the awareness of communication between tasks. We add communication into the scheduling model and provide an algorithm to minimize the amount of data transfer between tasks. Furthermore, we examine the performance of this heuristic and compare the schedules it produces with optimal solutions. Lastly, we introduce a scheduling and analysis fool that incorporates several scheduling heuristics. New heuristics are easily added to the tool. The goal of the tool is to help system designers/developers study the performance of different heuristics in scheduling real-time periodic tasks. The tool helps answer “what if” questions, which may also help designers tune their systems to achieve better performance while meeting deadlines

On electrical load tracking scheduling for a steel plant

Nolde and Morari (2010) study a steel manufacturing scheduling problem where the tasks must be scheduled such that electricity consumption matches to a pre-specified periodic energy chart. They propose a continuous time integer linear programming formulation to solve the problem. In this note, we present an alternative continuous time formulation, focused on the relative positions of tasks and time periods, that improves significantly the computation time

Optimal rate-based scheduling on multiprocessors

The PD2 Pfair/ERfair scheduling algorithm is the most efficient known algorithm for optimally scheduling periodic tasks on multiprocessors. In this paper, we prove that PD2 is also optimal for scheduling “rate-based” tasks whose processing steps may be highly jittered. The rate-based task model we consider generalizes the widely-studied sporadic task model