WattsApp: Power-Aware Container Scheduling

Abstract

Containers are popular for deploying workloads. However, there are limited software-based methods (hardware- based methods are expensive) for obtaining the power consumed by containers to facilitate power-aware container scheduling. This paper presents WattsApp, a tool underpinned by a six step software-based method for power-aware container scheduling to minimize power cap violations on a server. The proposed method relies on a neural network-based power estimation model and a power capped container scheduling technique. Experimental studies are pursued in a lab-based environment on 10 benchmarks on Intel and ARM processors. The results highlight that power estimation has negligible overheads - nearly 90% of all data samples can be estimated with less than a 10% error, and the Mean Absolute Percentage Error (MAPE) is less than 6%. The power-aware scheduling of WattsApp is more effective than Intel’s Running Power Average Limit (RAPL) based power capping as it does not degrade the performance of all running containers.

Publication
2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing