Efficient scheduling of sporadic, aperiodic, and periodic tasks with complex constraints

Abstract

Many industrial applications with real-time demands are composed of mixed sets of tasks with a variety of requirements. These can be in the form of standard timing constraints, such as periods and deadlines, or complex, e.g. to express application-specific or nontemporal constraints, reliability, performance, etc. Arrival patterns determine whether tasks are treated as periodic, sporadic or aperiodic. As many algorithms only focus on specific sets of task types and constraints, system design has to focus on those supported by a particular algorithm, at the expense of the rest. In this paper, we present an algorithm to deal with a combination of mixed sets of tasks and constraints: periodic tasks with complex and simple constraints, soft and firm aperiodic tasks and sporadic tasks. Instead of providing an algorithm tailored to a specific set of constraints, we propose an EDF (earliest deadline first) based runtime algorithm and the use of an offline scheduler for complexity reduction to transform complex constraints into the EDF model. At runtime, an extension to EDF, two-level EDF, ensures the feasible execution of tasks with complex constraints in the presence of additional tasks or overloads. We present an algorithm for handling offline guaranteed sporadic tasks, with minimum inter-arrival times, in this context, which keeps track of arrivals of instances of sporadic tasks to reduce pessimism about future sporadic arrivals and to improve the response times and acceptance of firm aperiodic tasks. A simulation study underlines the effectiveness of the proposed approach.