Semi-Federated Scheduling of Parallel Real-Time Tasks on Multiprocessors

TL;DR

This paper introduces semi-federated scheduling, reducing resource waste in parallel real-time task scheduling on multi-cores by sharing remaining capacity, and demonstrates its superior performance over existing methods.

Contribution

It proposes a novel semi-federated scheduling approach that minimizes resource waste by sharing capacity, improving efficiency over traditional federated scheduling.

Findings

Semi-federated scheduling outperforms federated scheduling.

Significant reduction in resource waste.

Experimental results show improved scheduling efficiency.

Abstract

Federated scheduling is a promising approach to schedule parallel real-time tasks on multi-cores, where each heavy task exclusively executes on a number of dedicated processors, while light tasks are treated as sequential sporadic tasks and share the remaining processors. However, federated scheduling suffers resource waste since a heavy task with processing capacity requirement $x + \epsilon$ (where $x$ is an integer and $0 < \epsilon < 1$) needs $x + 1$ dedicated processors. In the extreme case, almost half of the processing capacity is wasted. In this paper we propose the semi-federate scheduling approach, which only grants $x$ dedicated processors to a heavy task with processing capacity requirement $x + \epsilon$, and schedules the remaining $\epsilon$ part together with light tasks on shared processors. Experiments with randomly generated task sets show the semi-federated scheduling approach significantly outperforms not only federated scheduling, but also all existing approaches for scheduling parallel real-time tasks on multi-cores.