A Domain-Specific Language for Energy Management in Edge Computing Systems
Resumo
Energy consumption remains a major challenge in edge computing and IoT systems, especially in resource-constrained embedded platforms such as ESP32-based devices. Although Dynamic Frequency Scaling (DFS) and power-saving mechanisms can reduce energy consumption, integrating these mechanisms directly into embedded applications is complex and error-prone. This paper presents a declarative approach for energy-aware management in ESP32 edge applications using a Domain-Specific Language (DSL), automatic code instrumentation, and a lightweight runtime. The proposed approach allows developers to describe energy policies at a high level while the infrastructure automatically inserts runtime hooks and applies DFS-related decisions during execution. Preliminary results using ten FreeRTOS workloads indicate an average energy reduction of 22.40% while preserving application responsiveness.
Palavras-chave:
Energy Management, Edge Computing, ESP32, FreeRTOS, Domain-Specific Language, Dynamic Frequency Scaling
Referências
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S. K. Panda, M. Lin, and T. Zhou. Energy-Efficient Computation Offloading With DVFS Using Deep Reinforcement Learning. IEEE Internet of Things Journal, 2023.
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T. Lu, A. Kandemir, N. Vijaykrishnan, M. J. Irwin, and B. Shirazi. A Dynamic, Compiler Guided DVFS Mechanism to Achieve Energy-Efficiency in Multi-Core Processors. Sustainable Computing: Informatics and Systems, 12:1–9, 2016.
J. Zidar, T. Matic, I. Aleksic, and Z. Hocenski. Dynamic Voltage and Frequency Scaling as a Method for Reducing Energy Consumption in Ultra-Low-Power Embedded Systems. Electronics, 13(5):826, 2024.
M. Satyanarayanan. The Emergence of Edge Computing. Computer, 50(1):30–39, 2017.
Y. Mao, C. You, J. Zhang, and K. B. Letaief. A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys & Tutorials, 19(4):2322–2358, 2017.
B. Kopras, F. Idzikowski, and H. Bogucka. A Survey on Reduction of Energy Consumption in Fog Networks. Sensors, 24(18):6064, 2024.
S. K. Panda, M. Lin, and T. Zhou. Energy-Efficient Computation Offloading With DVFS Using Deep Reinforcement Learning. IEEE Internet of Things Journal, 2023.
M. Mernik, J. Heering, and A. M. Sloane. When and How to Develop Domain-Specific Languages. ACM Computing Surveys, 2005.
A. van Deursen, P. Klint, and J. Visser. Domain-Specific Languages: An Annotated Bibliography. SIGPLAN Notices, 2000.
T. Lu, A. Kandemir, N. Vijaykrishnan, M. J. Irwin, and B. Shirazi. A Dynamic, Compiler Guided DVFS Mechanism to Achieve Energy-Efficiency in Multi-Core Processors. Sustainable Computing: Informatics and Systems, 12:1–9, 2016.
J. Zidar, T. Matic, I. Aleksic, and Z. Hocenski. Dynamic Voltage and Frequency Scaling as a Method for Reducing Energy Consumption in Ultra-Low-Power Embedded Systems. Electronics, 13(5):826, 2024.
Publicado
08/09/2026
Como Citar
VIDAL, Victor de Oliveira; OLIVEIRA, Horácio; BARRETO, Raimundo da Silva.
A Domain-Specific Language for Energy Management in Edge Computing Systems. In: SIMPÓSIO BRASILEIRO DE LINGUAGENS DE PROGRAMAÇÃO (SBLP), 30. , 2026, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 168-172.
DOI: https://doi.org/10.5753/sblp.2026.27624.
