Heats: Heterogeneity-and Energy-Aware Task-Based Scheduling

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.

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

“…Finally, another trend worth-mentioning is the need to reduce energy consumption in data centers, with multiple vendors such as Amazon AWS [4], Google [45], Microsoft [78] committing eforts in this direction. As a result, diferent works such as [50,52,89,106] have been proposed to address the energy eiciency and carbon footprint concerns.…”

“…The proposal in [89] starts from the observation that there is a tradeof between the need to reduce a cluster's energy consumption and the use of high-performance hardware nodes. As a result, they propose a custom scheduler, called HEATS.…”

“…• Hardware type [89]: When the cluster is composed of hardware nodes of varying energy consumption patterns and various performance levels, the scheduler can favor less powerful nodes, which consume less energy, as long as the user explicitly tolerates the performance loss that may occur.…”

LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing

“…Every x seconds the rescheduling phase is triggered for the set of all running tasks. If the re-execution of the best fit decides on a different target node, the task is migrated to the new host and removed from the current one [10].…”

A multi-resource scheduling scheme of Kubernetes for IIoT

“…Kubernetes is an open-source docker container management system of Google, which originated from Google's longstanding Borg system [1]. And Kubernetes has been applied to resource scheduling in IIoT scenarios [2]. Currently, Kubernetes is widely used, and its scheduling strategy has also been widely studied.…”