Task scheduling using Ant Colony Optimization in multicore architectures: a survey

Abstract

The problem of determining a set of real-time tasks that can be assigned to the multiprocessors and finding a feasible solution of scheduling these tasks among the multiprocessors is a challenging issue and known to be NP-complete. Many applications today require extensive computing power than traditional uniprocessors can offer. Parallel processing provides a cost-effective solution to this problem by increasing the number of CPUs by adding an efficient communication system between them which results much higher computing power to solve compute-intensive problems. Multiprocessor task scheduling is the key research area in high performance computing, and the goal of the task scheduling is to minimize makespan. This paper discusses various approaches adopted to solve task scheduling problem in multiprocessor systems with a bio-inspired swarm system paradigm, the Ant Colony Optimization (ACO) since ACO algorithm leads to the fair load balancing among the processors and reducing the waiting time of the tasks. The parameters such as execution time, communication cost, cache performance, total power consumption, energy consumption, high system utilization, task pre-emptions were studied to compare the task scheduling algorithms.