Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice

TL;DR

This paper investigates runtime algorithms for energy-efficient scheduling of task graphs on MPSoCs, balancing optimality and computational feasibility, with practical evaluation on benchmarks and synthetic problems.

Contribution

It introduces and analyzes algorithms for real-time energy minimization in DAG scheduling on MPSoCs, including optimal and approximation methods with proven guarantees.

Findings

Algorithms achieve near-optimal energy savings at runtime.

Practical algorithms are feasible for real-world embedded benchmarks.

Theoretical insights guide the trade-off between solution quality and computation time.

Abstract

Static (offline) techniques for mapping applications given by task graphs to MPSoC systems often deliver overly pessimistic and thus suboptimal results w.r.t. exploiting time slack in order to minimize the energy consumption. This holds true in particular in case computation times of tasks may be workload-dependent and becoming known only at runtime or in case of conditionally executed tasks or scenarios. This paper studies and quantitatively evaluates different classes of algorithms for scheduling periodic applications given by task graphs (i.e., DAGs) with precedence constraints and a global deadline on homogeneous MPSoCs purely at runtime on a per-instance base. We present and analyze algorithms providing provably optimal results as well as approximation algorithms with proven guarantees on the achieved energy savings. For problem instances taken from realistic embedded system benchmarks as well as synthetic scalable problems, we provide results on the computation time and quality of each algorithm to perform a) scheduling and b) voltage/speed assignments for each task at runtime. In our portfolio, we distinguish as well continuous and discrete speed (e.g., DVFS-related) assignment problems. In summary, the presented ties between theory (algorithmic complexity and optimality) and execution time analysis deliver important insights on the practical usability of the presented algorithms for runtime optimization of task scheduling and speed assignment on MPSoCs.