Cloud servers face growing energy demands, imposing operational costs and challenges for sustainability goals. The proliferation of microservice architectures presents opportunities for energy management, offering modularity and scalability. However, microservices may introduce energy inefficiencies due to excessive inter-service communication and mismanaged scaling. Existing solutions commonly focus on hardware-level optimization, overlooking application-level energy consumption. This research aims to integrate energy metrics into the lifecycle of microservice-based applications to enable energy-aware decision-making. Using the MAPE-K framework, we enhance Monitoring, Analysis, and Knowledge components with energy metrics. Contributions include runtime monitoring tooling, energy-aware distributed tracing, and reconfiguration strategies, with initial results validated using the TeaStore benchmark application deployed on Grid’5000 testbed. The research will deliver tools and strategies for improving energy efficiency in microservice-based applications, benefiting cloud providers, architects, and end-users striving for sustainable operations.