# HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling

## TL;DR

HEATS is a novel energy-aware task scheduling system for heterogeneous cloud clusters that learns hardware performance and energy profiles to optimize container deployment, reducing energy consumption with minimal impact on runtime.

## Contribution

This work introduces HEATS, a new energy-aware, task-based scheduler integrated into Kubernetes that exploits hardware heterogeneity and energy profiles for improved efficiency.

## Key findings

- Up to 8.5% energy savings achieved.
- Runtime impact limited to 7%.
- Effective in heterogeneous cloud environments.

## Abstract

Cloud providers usually offer diverse types of hardware for their users. Customers exploit this option to deploy cloud instances featuring GPUs, FPGAs, architectures other than x86 (e.g., ARM, IBM Power8), or featuring certain specific extensions (e.g, Intel SGX). We consider in this work the instances used by customers to deploy containers, nowadays the de facto standard for micro-services, or to execute computing tasks. In doing so, the underlying container orchestrator (e.g., Kubernetes) should be designed so as to take into account and exploit this hardware diversity. In addition, besides the feature range provided by different machines, there is an often overlooked diversity in the energy requirements introduced by hardware heterogeneity, which is simply ignored by default container orchestrator's placement strategies. We introduce HEATS, a new task-oriented and energy-aware orchestrator for containerized applications targeting heterogeneous clusters. HEATS allows customers to trade performance vs. energy requirements. Our system first learns the performance and energy features of the physical hosts. Then, it monitors the execution of tasks on the hosts and opportunistically migrates them onto different cluster nodes to match the customer-required deployment trade-offs. Our HEATS prototype is implemented within Google's Kubernetes. The evaluation with synthetic traces in our cluster indicate that our approach can yield considerable energy savings (up to 8.5%) and only marginally affect the overall runtime of deployed tasks (by at most 7%). HEATS is released as open-source.