Temporal consistency maintenance on multiprocessor platforms with instance skipping

Maintaining temporal consistency of real-time data is important for cyber-physical systems.Most of the previous studies focus on uniprocessor systems.In this paper,the problem of temporal consistency maintenance on multiprocessor platforms with instance skipping was formulated based on the(m,k)-constrained model.A partitioned scheduling method SC-AD was proposed to solve the problem.SC-AD uses a derived sufficient schedulability condition to calculate the initial value of m for each sensor transaction.It then partitions the transactions among the processors in a balanced way.To further reduce the average relative invalid time of real-time data,SC-AD judiciously increases the values of m for transactions assigned to each processor.Experiment results show that SC-AD outperforms the baseline methods in terms of the average relative invalid time and the average valid ratio under different system workloads.

An incremental ant colony optimization based approach to task assignment to processors for multiprocessor scheduling

Optimized task scheduling is one of the most important challenges to achieve high performance in multiprocessor environments such as parallel and distributed systems. Most introduced task-scheduling algorithms are based on the so-called list scheduling technique. The basic idea behind list scheduling is to prepare a sequence of nodes in the form of a list for scheduling by assigning them some priority measurements, and then repeatedly removing the node with the highest priority from the list and allocating it to the processor providing the earliest start time （EST）. Therefore, it can be inferred that the makespans obtained are dominated by two major factors： （1） which order of tasks should be selected （sequence subproblem）; （2） how the selected order should be assigned to the processors （assignment subproblem）. A number of good approaches for overcoming the task sequence dilemma have been proposed in the literature, while the task assignment problem has not been studied much. The results of this study prove that assigning tasks to the processors using the traditional EST method is not optimum; in addition, a novel approach based on the ant colony optimization algorithm is introduced, which can find far better solutions.

A SIMPLE PROOF OF THE INEQUALITY R_M(MF(κ))≤1.2+(1/2~κ) IN MULTIPROCESSOR SCHEDULING

We consider the well-known problem of scheduling n independent tasks nonpreemptivelyon m identical processors with the objective of minimizing the makespan. Coffman, Garey andJohnson described an algorithm MULTIFIT, based on bin-packing, with a worst case performancebetter than the LPT-algorithm. The bound 1.22 obtained by them was claimed by Friesen in1984 that it can be improved to 1.2. In this paper we give a simp1e proof for this bound.

Improved Blocking Time Analysis and Evaluation for the Multiprocessor Priority Ceiling Protocol

The Multiprocessor Priority Ceiling Protocol （MPCP） is a classic suspension-based real-time locking protocol for partitioned fixed-priority （P-FP） scheduling. However, existing blocking time analysis is pessimistic under the P-FP ＋ MPCP scheduling, which negatively impacts the schedulability for real-time tasks. In this paper, we model each task as an alternating sequence of normal and critical sections, and use both the best-case execution time （BCET） and the worst-case execution time （WCET） to describe the execution requirement for each section. Based on this model, a novel analysis is proposed to bound shared resource requests. This analysis uses BCET to derive the lower bound on the inter-arrival time for shared resource requests, and uses WCET to obtain the upper bound on the execution time of a task on critical sections during an arbitrary time interval of △t. Based on this analysis, improved blocking analysis and its associated worst-case response time （WCRT） analysis are proposed for P-FP ＋ MPCP scheduling. Schedulability experiments indicate that the proposed method outperforms the existing methods and improves the schedulability significantly.