Towards Platform Specific Energy Estimation for Executable Domain-Specific Modeling Languages

Energy consumption is becoming a major subject when designing, developing and running programs. Most developers code and run their applications in an energy oblivious manner, mostly because of a lack of energy-related knowledge about their system. This problem also exists in the realm of executable...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) S. 314 - 317
Hauptverfasser: Beziers la Fosse, Thibault, Tisi, Massimo, Bousse, Erwan, Mottu, Jean-Marie, Sunye, Gerson
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2019
Schlagworte:
Adaptation models
Codes
Domain specific languages
Energy consumption
Energy Estimation
Energy measurement
Engines
Estimation
Model driven engineering
Semantics
xDSMLs
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Energy consumption is becoming a major subject when designing, developing and running programs. Most developers code and run their applications in an energy oblivious manner, mostly because of a lack of energy-related knowledge about their system. This problem also exists in the realm of executable domain-specific modeling languages, where end-users create models conforming to a given meta-model and execute them with little knowledge about their operational semantic and related energy consumption. In this work, we propose a domain-specific language for decorating meta-models of executable languages with platform-specific energy estimation formulas. We also extend the GEMOC execution engine to dynamically perform energy estimations on any executable model conforming to the decorated meta-model. The energy estimation model defined can then be easily adapted to other models and platforms, without requiring any measurement tooling or knowledge from the end-user.
DOI:10.1109/MODELS-C.2019.00048