Energy-Aware Scheduling in Cloud-Native Environment

Energy efficiency is becoming increasingly important for sustainability. In a cloud-native environment, orchestration platforms such as Kubernetes can significantly impact the overall energy performance of applications as they manage their lifecycle, including scheduling. Kubernetes, specifically as a de facto cloud platform, has a default scheduler that does not account for energy consumption in its decision-making process, highlighting the need to explore mechanisms that can improve the energy performance of deployed applications. In this paper, we present an energy-aware scheduling mechanism within Kubernetes to optimize the energy usage of cloud-native applications while meeting other scheduling requirements and constraints. This approach extends the scheduling logic in Kubernetes through plugins that integrate energy metrics and employ Machine Learning-based power estimation for energy-optimized decision-making. The evaluation of the proposed approach shows up to 12 % energy savings with minimal performance degradation. When complemented by other consolidation techniques, it could potentially lead to substantial savings ranging from 21 % to 65 %, depending on the system load in the scenario analyzed. Importantly, this method does not introduce significant overhead.