Multiprocessor Real-Time Scheduling Considering Concurrency and Urgency

It has been widely studied how to schedule real-time tasks on multiprocessor platforms. Several studies find optimal scheduling policies for implicit deadline task systems, but it is hard to understand how each policy utilizes the two important aspects of scheduling real-time tasks on multiprocessors: inter-job concurrency and job urgency. In this paper, we introduce a new scheduling policy that considers these two properties. We prove that the policy is optimal for the special case when the execution time of all tasks are equally one and deadlines are implicit, and observe that the policy is a new concept in that it is not an instance of Pfair or ERfair. It remains open to find a scheduliability condition for general task systems under our scheduling policy

Systematic Searches for Global Multiprocessor Real-Time Scheduling

In this paper we address the problem of global real-time periodic scheduling on homogeneous multiprocessor platforms. A number of theoretical results have been obtained in the field of real-time systems, but mainly focusing on properties of specific algorithms in uniprocessor settings. The multiprocessor case has been considered only recently, with few resolution techniques proposed and experimented with up to now. In this paper we discuss several systematic search algorithms—exploring different search spaces—that exploit various features of the problem. These approaches are then evaluated experimentally on numerous randomly generated problems. This work shows (1) how two heuristic approaches can solve most (feasible and unfeasible) problems in no time, and (2) how to improve a state of the art algorithm by looking at jobs' laxities and by focusing the search on bottlenecks. We also discuss limitations of the proposed solvers and future work

An off-line multiprocessor real-time scheduling algorithm to reduce static energy consumption

International audienceEnergy consumption of highly reliable real-time embedded systems is a significant concern. Static energy consumption tends to become more important than dynamic energy consumption. This paper aims to propose a new off-line scheduling algorithm to put as much as possible processors in low- power states instead of idling. In these states, energy consumption is reduced, enhancing the battery life-time of mission critical systems. However, no instruction can be executed and a transition delay is required to come back to the active state. Activating deeper low-power states requires to produce larger idle periods. As the processor usage is constant for a given task set, this objective implies reducing the number of idle periods. Our proposal is to modelize the processors idle time as an additional task. Then we formalize the problem as a linear equation system with the objective of reducing the number of preemptions (or executions) of this additional task. Simulations show that our algorithm is more energy efficient than existing algorithms

The multiprocessor real-time scheduling of general task systems

The recent emergence of multicore and related technologies in many commercial systems has increased the prevalence of multiprocessor architectures. Contemporaneously, real-time applications have become more complex and sophisticated in their behavior and interaction. Inevitably, these complex real-time applications will be deployed upon these multiprocessor platforms and require temporal analysis techniques to verify their correctness. However, most prior research in multiprocessor real-time scheduling has addressed the temporal analysis only of Liu and Layland task systems. The goal of this dissertation is to extend real-time scheduling theory for multiprocessor systems by developing temporal analysis techniques for more general task models such as the sporadic task model, the generalized multiframe task model, and the recurring real-time task model. The thesis of this dissertation is: Optimal online multiprocessor real-time scheduling algorithms for sporadic and more general task systems are impossible; however, efficient, online scheduling algorithms and associated feasibility and schedulability tests, with provably bounded deviation from any optimal test, exist. To support our thesis, this dissertation develops feasibility and schedulability tests for various multiprocessor scheduling paradigms. We consider three classes of multiprocessor scheduling based on whether a real-time job may migrate between processors: full-migration, restricted-migration, and partitioned. For all general task systems, we obtain feasibility tests for arbitrary real-time instances under the full-and restricted-migration paradigms. Despite the existence of tests for feasibility, we show that optimal online scheduling of sporadic and more general systems is impossible. Therefore, we focus on scheduling algorithms that have constant-factor approximation ratios in terms of an analysis technique known as resource augmentation. We develop schedulability tests for scheduling algorithms, earliest-deadline-first (edf) and deadline-monotonic (dm), under full-migration and partitioned scheduling paradigms. Feasibility and schedulability tests presented in this dissertation use the workload metrics of demand-based load and maximum job density and have provably bounded deviation from optimal in terms of resource augmentation. We show the demand-based load and maximum job density metrics may be exactly computed in pseudo-polynomial time for general task systems and approximated in polynomial time for sporadic task systems

Global Multiprocessor Real-Time Scheduling as a Constraint Satisfaction Problem

International audienceIn this paper we address the problem of global real-time periodic scheduling on heterogeneous multiprocessor platforms. We give a solution based on a {\em constraint satisfaction problem} that we prove equivalent to the multiprocessor problem. A solution has to satisfy a set of constraints and there is no performance criterion to optimize. We propose two different CSP formulations. The first one is a basic encoding allowing to use state of the art CSP solvers. The second one is a more complex encoding designed to obtain solutions faster. With these encodings, we then study the resolution of the scheduling problem using systematic search algorithms based on backtracking

A Constant-Approximate Feasibility Test for Multiprocessor Real-Time Scheduling

We devise the first constant-approximate feasibility test for sporadic multiprocessor real-time scheduling. We give an algorithm that, given a task system and e > 0, correctly decides either that the task system can be scheduled using the earliest deadline first algorithm on m speed-(2-1/m+e) machines, or that the system is infeasible for m speed-1 machines. The running time of the algorithm is polynomial in the size of the task system and 1/e. We also provide an improved bound trading off speed for additional machines

An Average-Case Analysis for Rate-Monotonic Multiprocessor Real-time Scheduling

We introduce the "First Fit Matching Periods" algorithm for static-priority multiprocessor scheduling of periodic tasks with implicit deadlines and show that it yields asymptotically optimal processor assignments if utilization values are chosen uniformly at random. More precisely we prove that the expected waste is upper bounded by O(n^(3/4) * (log n)^(3/8)). Here the waste denotes the ratio of idle times, cumulated over all processors and n gives the number of tasks. The algorithm can be implemented to run in time O(n log n) and even in the worst case, an asymptotic approximation ratio of 2 is guaranteed. Experiments yield an expected waste proportional to n^0.70, indicating that the above upper bound on the expected waste is almost tight