A genetic algorithm based approach to maximizing real-time system value under resource constraints

For many embedded systems, different real-time applications are consolidated to the same hardware platform to meet the growing demand for diverse functionalities. Due to functionality differences, the values that different applications contribute to the system may not be the same. When system resources are limited and not all applications can be executed with guaranteed QoS, decisions have to be made as to which applications should be selected and how their tasks are deployed on available processors so that the system value is maximized and all the selected applications meet their deadlines. However, making the optimal decision for the application selection and task deployment (ASTD) problem is NP-hard. In this paper, we present a genetic algorithm (GA) based approach for the ASTD problem. We experimentally compare the performance of GA-based approach with the optimal approach chosen by enumerating all possible choices on a small scale, and with other heuristic approaches existed in the literature on a large scale. The results show that the system value obtained by the GA-based approach is close to the optimal value and can be twice as large as the value obtained by other heuristic approaches.