Bicriteria Continuous Tasks Scheduling Using Genetic Algorithm

Real-time systems are characterized by computational activities with timing constraints and classified into two categories: hard real-time systems and soft real-time systems. In hard real-time systems, missing deadlines can be catastrophic. However, in the case of soft real-time systems, slight violence of deadlines is not so critical. In multimedia systems, especially, continuous media is one of typical task scheduling of soft real-time systems. In this paper, we propose a new real-time tasks scheduling algorithm using Proportion-based Genetic Algorithm (pp-GA). Especially continuous tasks are considered on uniprocessor soft real-time environment. The objective of proposed scheduling algorithm is to minimize the variance of deadline missing and the total number of context switch among tasks. For these objectives, this paper combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through a simulation study.In simulation studies, the results of proposed algorithm show better than that of other algorithms.