Multiprocessor Real-time Scheduling Research Articles

Multiprocessor real-time scheduling

Multiprocessor real-time scheduling

Feasibility Analysis of Sporadic Real-Time Multiprocessor Task Systems

We give the first algorithm for testing the feasibility of a system of sporadic real-time tasks on a set of identical processors, solving one major open problem in the area of multiprocessor real-time scheduling [S.K. Baruah and K. Pruhs, Journal of Scheduling, 2009]. We also investigate the related notion of schedulability and a notion that we call online feasibility. Finally, we show that discrete-time schedules are as powerful as continuous-time schedules, which answers another open question in the above mentioned survey.

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

We devise an approximate feasibility test for multiprocessor real-time scheduling in the sporadic task model. We give an algorithm that, given a task system and ε>0, correctly decides either that the task system can be scheduled using the Earliest Deadline First algorithm on m speed-(2−1/m+ε) machines, or that the system is not schedulable by any algorithm on m unit speed machines. This speedup bound is known to be the best possible for EDF. The running time of the algorithm is polynomial in the size of the task system and 1/ε. We also provide a generalized tight bound that trades off speed with additional machines.

Reducing network and computation complexities in neural based real-time scheduling scheme

Multiprocessor real-time scheduling is an important issue in many applications. A neural network provides a highly effective method to obtain good solutions for real-time scheduling problems. However, multiprocessor real-time scheduling problems include multiple variables; processor, process and time, and the neural networks have to be presented in three dimensions with these variables. Hence, the corresponding neural networks have more neurons, and synaptic weights, and thus associated network and computational complexities increase. Meanwhile, a neural network using the competitive scheme can provide a highly effective method with less network complexity. Therefore, in this study, a simplified two-dimensional Hopfield-type neural network using competitive rule is introduced for solving three-dimensional multiprocessor real-time scheduling problems. Restated, a two-dimensional network is proposed to lower the neural network dimensions and decrease the number of neurons and hence reduce the network complexity; an M- out- of- N competitive scheme is suggested to greatly reduce the computational complexity. Simulation results reveal that the proposed scheme imposed on the derived energy function with respect to process time and deadline constraints is an appropriate approach to solving these class scheduling problems. Moreover, the computational complexity of the proposed scheme is greatly lowered to O( N × T 2).

Optimizing Distributed Real-Time Embedded System Handling Dependence and Several Strict Periodicity Constraints

This paper focuses on real-time nonpreemptive multiprocessor scheduling with precedence and strict periodicity constraints. Since this problem is NP-hard, there exist several approaches to resolve it. In addition, because of periodicity constraints our problem stands for a decision problem which consists in determining if, a solution exists or not. Therefore, the first criterion on which the proposed heuristic is evaluated is its schedulability. Then, the second criterion on which the proposed heuristic is evaluated is its execution time. Hence, we performed a schedulability analysis which leads to a necessary and sufficient schedulability condition for determining whether a task satisfies its precedence and periodicity constraints on a processor where others tasks have already been scheduled. We also present two multiperiodic applications.

Multiprocessor real-time scheduling considering concurrency and urgency

It has been widely studied how to schedule realtime 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.

Eliminating Migration in Multi-processor Scheduling

We investigate the power of migration in real-time multi-processor scheduling with preemption. We show that every collection of jobs that can be completed by some schedule S on m processors can also be completed by a nonmigratory schedule S′ on 6m−5 processors. We can conclude from this result that, for many scheduling problems, such as P∣ri,pmtn∣∑wi(1−Ui) and special cases thereof, the ability of the scheduler to migrate jobs is of only limited advantage. Our proof is constructive, and can be implemented to run in pseudo-polynomial time (or in polynomial time at the cost of raising the bound from 6m−5 to 12m−5). As an example of the usefulness of this result, we give a polynomial-time 6-approximation reduction from the problem P∣ri,pmtn∣∑wi(1−Ui) to the problem 1∣ri,pmtn∣∑wi(1−Ui). The proof of correctness of this reduction critically uses the fact that there is a nonmigratory schedule that closely approximates the optimal migratory schedule.