Power Budgeting of Big Data Applications in Container-based Clusters

Energy consumption is currently highly regarded on computing systems for many reasons, such as improving the environmental impact and reducing operational costs considering the rising price of energy. Previous works have analysed how to improve energy efficiency from the entire infrastructure down to individual computing instances (e.g., virtual machines). However, the research is more scarce when it comes to controlling energy consumption, specially in real time and at the software level. This paper presents a platform that manages a power budget to cap the energy consumed from users to applications and down to individual instances. Using containers as virtualization technology, the energy limitation is implemented thanks to the platform's ability to monitor container energy consumption and dynamically adjust its CPU resources via vertical scaling as required. Representative Big Data applications have been deployed on the platform to prove the feasibility of this approach for energy control, showing that it is possible to distribute and enforce a power budget among users and applications.