WattsApp: Power-Aware Container Scheduling

TL;DR

WattsApp introduces a software-based, neural network-powered container scheduling tool that effectively manages power consumption and prevents violations without performance degradation in distributed systems.

Contribution

The paper presents WattsApp, a novel power-aware container scheduling method using neural networks for accurate power estimation and efficient power capping.

Findings

Power estimation model has less than 10% error for 90% of data samples.

WattsApp outperforms RAPL-based power capping in effectiveness.

Power estimation overhead is negligible.

Abstract

Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensive requiring hardware level changes) for obtaining the power consumed by containers for facilitating power-aware container scheduling, an essential activity for efficient management of distributed systems. 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 deployed on Intel and ARM processors. The results highlight that the power estimation model has negligible overheads for data collection - 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 for both single and multiple containers as it does not degrade the performance of all containers running on the server. The results confirm the feasibility of WattsApp.