Energy-efficient scheduling of throughput-constrained streaming applications by periodic mode switching

Abstract

In this paper, we address the problem of energy reduction when scheduling streaming applications with throughput constraints on homogeneous multiprocessor systems with voltage and frequency scaling capability. We propose a novel periodic scheduling framework which allows streaming applications to switch their execution periodically between a few energy-efficient schedules at run-time in order to meet a throughput constraint at long run. Using such periodic switching, we can benefit from adopting Dynamic Voltage and Frequency Scaling (DVFS) techniques to exploit available static slack time in the schedule of an application efficiently. The experimental results, on a set of real-life streaming applications, show that our novel scheduling approach can achieve up to 68% energy reduction depending on the application and the throughput constraint compared to related approaches.

Modeling, Analysis, and Hard Real-Time Scheduling of Adaptive Streaming Applications

“…The major difference between these models in this case study is their transition protocol which is the MOO protocol in our MADF model and the self-timed protocol in FSM-SADF. Another example of the application of our MOO protocol can be found in [20].…”