A binary integer linear programming-based approach for solving the allocation problem in multiprocessor partitioned scheduling

Abstract

Scheduling is a main issue of real-time systems because it involves meeting the deadlines. In this paper, we address the problem of scheduling a set of periodic tasks on m processors under EDF (Earliest Deadline First) using a partitioned scheme. The allocation problem is transformed into a binary integer linear program. Then, it is solved by applying Geoffrion's version of Balas' additive method, optimized for the real-time scheduling problem. In order to assess the feasibility of the approach for a small size practical problem, some experimental results are shown.

Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

“…The particle's position updates present the next position of the task. As the particle position updates, we have mentioned formula (7) in the third section, CP = CP + . When > 0, it indicates that it needs to adjust the number of the processor, and then, CP = ; otherwise, the position of the particle remains unchanged; that is, CP = CP .…”

“…Although it is an NP-hard problem, there are many approximation algorithms for solving the problem of realtime task allocation in a heterogeneous processor environment, including traditional real-time task scheduling algorithms such as deadline-monotonic (DM) algorithm [4], rate-monotonic (RM) algorithm [5], least-laxity-first (LLF) algorithm [6], earliest-deadline-first (EDF) algorithm [5], and linear programming-based (LP) algorithm [7] and the swarm intelligence algorithms such as ant colony optimization (ACO) [8], genetic algorithm (GA) [9][10][11], and shuffled frog-leaping algorithm (SFLA) [12,13]. In these studies, most algorithms do not consider energy consumption factors.…”