An Evaluation of Adaptive Partitioning of Real-Time Workloads on Linux

This paper provides an open implementation and an experimental evaluation of an adaptive partitioning approach for scheduling real-time tasks on symmetric multicore systems. The proposed technique is based on combining partitioned EDF scheduling with an adaptive migration policy that moves tasks across processors only when strictly needed to respect their temporal constraints. The implementation of the technique within the Linux kernel, via modifications to the SCHED DEADLINE code base, is presented. An extensive experimentation has been conducted by applying the technique on a real multi-core platform with several randomly generated synthetic task sets. The obtained experimental results highlight that the approach exhibits a promising performance to schedule real-time workloads on a real system, with a greatly reduced number of migrations compared to the original global EDF available in SCHED DEADLINE.