Evolving scheduling heuristics with genetic programming for optimization of quality of service in weakly hard real-time systems

Abstract

The weakly hard real-time system model is used for describing the real-time systems that allow occasional violations of real-time timing constraints. These systems include real-time control systems, multimedia systems, and communication systems. In some approaches that deal with mitigating the system overload in real-time systems with periodic tasks, namely job-skipping algorithms, the constraints defined by the weakly hard real-time model are used as a mechanism for defining the pattern of task instances (jobs) that may be skipped in order to reduce the system load. The performance of these algorithms is usually evaluated with respect to the quality of service metric, which depends on the number of skipped jobs. In this work, we investigate the possibility of using genetic programming in the automated synthesis of scheduling heuristics for optimizing skipping patterns in order to increase the average quality of service in comparison with the conventional job-skipping algorithms. Using genetic programming to automatically synthesize heuristics allows for an easy and quick design of novel heuristics for various problem types and optimization criteria. We present two different approaches for implementing the proposed method. The first approach is to encapsulate the evolved heuristics into job-skipping algorithms known from the literature, namely Red Tasks as Late as Possible (RLP) and Blue When Possible (BWP). The idea of the second approach is to employ the evolved heuristics as standalone job-skipping algorithms. The results show an improvement of up to 15% in comparison with the state-of-the-art algorithms. The novel methods described in this work present a significant upgrade of the standard job-skipping algorithms as they provide a notable improvement in terms of quality of service while ensuring the fulfillment of weakly hard constraints. Moreover, the presented methods are computationally efficient and are therefore suitable for implementation on real-time operating systems, which is not the case with similar methods based on optimization techniques.