A Deadline-Partition Oriented Heterogeneous Multi-Core Scheduler for Periodic Tasks

Abstract

Real-time systems are increasingly being implemented on heterogeneous multi-core platforms to efficiently cater to their diverse and high computation demands. Over the years, researchers have developed mechanisms to efficiently schedule tasks on homogeneous multi-cores such that all tasks meet their execution and deadline requirements. However, devising an efficient scheduling strategy for real-time tasks on heterogeneous platforms has proved to be a challenging as well as computationally expensive problem. Today, there is a severe dearth of low-overhead techniques towards real-time scheduling on heterogeneous platforms. Hence, we propose an effective low-overhead heuristic approach for scheduling a set of periodic tasks executing on a heterogeneous multi-core platform. Employing the concept of deadline partitioning to obtain a set of discrete time slices, we propose a scheme to efficiently schedule tasks over these time slices while incurring low and bounded number of migrations. Conducted experiments have shown promising results and indicate to the practical efficacy of our approach.