Exploiting Job Variability to Minimize Energy Consumption under Real-Time Constraints

Exploitation de la variabilité des tâches pour minimiser la consommation d'énergie sous des contraintes temps-réels

Bruno Gaujal (1, 2) , Girault Alain (3) , Stéphan Plassart (2, 3)

1 MESCAL - Middleware efficiently scalable
2 POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems
3 SPADES - Sound Programming of Adaptive Dependable Embedded Systems

This paper proposes a Markov Decision Process (MDP) approach to compute theoptimal on-line speed scaling policy that minimizes the energy consumption of a single processorexecuting a finite or infinite set of jobs with real-time constraints, in the non-clairvoyant case,i.e., when the actual execution time of the jobs is unknown when they are released. In real lifeapplications, it is common at release time to know only the Worst-Case Execution Time of a job,and theactualexecution time of this job is only discovered when it finishes. Choosing the processorspeed purely in function of the Worst-Case Execution Time is sub-optimal. When the probabilitydistribution of the actual execution time is known, it is possible to exploit this knowledge tochoose a lower processor speed so as to minimize the expected energy consumption (while stillguaranteeing that all jobs meet their deadline). Our MDP solution solves this problem optimallywith discrete processor speeds. Compared with approaches from the literature, the gain offeredby the new policy ranges from a few percent when the variability of job characteristics is small, tomore than50%when the job execution time distributions are far from their worst case.

Mots clés: Markov Decision Process; Real-Time Systems; Optimization; Dynamic Voltage & Frequency Scaling

Domaines: Informatique [cs]

Bruno Gaujal, Girault Alain, Stéphan Plassart. Exploiting Job Variability to Minimize Energy Consumption under Real-Time Constraints. [Research Report] Inria Grenoble Rhône-Alpes, Université de Grenoble; Université Grenoble - Alpes. 2019. ⟨hal-02371742v1⟩