Cloud computing provides many benefits, such as virtualization, high availability, low cost of maintenance, and elasticity. These characteristics provide a flexible resource management, transparent to users, giving the illusion of access to almost unlimited resources. However, the increase of the energy consumption of cloud environments turns light over the amount of hardware resources provisioned in virtual machines. Self-adaptive systems are designed as a way to avoid and correct, under certain constraints, the degradation of quality of service (QoS) of software during its execution, which may be due to changes in environmental conditions. Self-adaptation brings elasticity to cloud-based applications by providing various configurations, thus avoiding the degradation of their QoS. Recent works in the literature on self-adaptive cloud systems (SACS) mainly focus on the analysis of the CPU usage and resource allocation for running cloud applications, neglecting the analysis of their impact on energy consumption. This PhD proposes to investigate how to integrate energy efficiency and energy awareness, as QoS criteria, in a self-adaptive architecture for cloud applications. It implies to study what are the necessary elements of a self-adaptive architecture of cloud applications at design-time, and how these elements should be exploited at run-time to ensure energy efficiency without impacting the performance of cloud applications.