SAFLA: Scheduling Multiple Real-Time Periodic Task Graphs on Heterogeneous Systems

Abstract

Many modern Cyber Physical Systems (CPSs) are composed of multiple independent periodically executing real-time control tasks having inter-dependent component sub-tasks. Each such control task is therefore usually represented as <i>Directed-acyclic Task Graphs</i> (DTGs). These CPSs are often distributed in nature and are quickly shifting from homogeneous to heterogeneous processing platforms in order to meet ever increasing demands for performance and energy savings, within limited resource budgets. In spite of the practical relevance of the problem in today's CPS design scenario, very few research works in literature have tried to address this due to its inherent computational as well as design complexity. This work endeavors to solve the problem of co-scheduling a set of periodic real-time applications each modelled as an independent DTG, to be executed on a distributed platform consisting of heterogeneous processors communicating using shared buses. Assuming the processing platform to be DVFS (Dynamic Voltage Frequency Scaling) enabled, we attempt to minimize dynamic energy dissipation associated with the execution of all DTGs over an hyperperiod <inline-formula><tex-math notation="LaTeX">$\mathcal {H}$</tex-math></inline-formula> while ensuring that no DTG instance within <inline-formula><tex-math notation="LaTeX">$\mathcal {H}$</tex-math></inline-formula> misses its deadline. The problem has first been formally represented as a constraint optimization problem. However, an optimal solution using standard solvers become prohibitively compute as well as memory intensive and doesn't scale even for moderate problem sizes. Hence, in this work, we attempt to develop a three-phase list-based hierarchical scheduling algorithm called <i>Slack Aware Frequency Level Allocator</i> ( <i>SAFLA</i> ). The efficacy of <i>SAFLA</i> has been critically evaluated through simulation using benchmark DTGs.