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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 certainspecific extensions (e.g, Intel SGX). We consider in this work the instancesused by customers to deploy containers, nowadays the de facto standard formicro-services, or to execute computing tasks. In doing so, the underlyingcontainer orchestrator (e.g., Kubernetes) should be designed so as to take intoaccount and exploit this hardware diversity. In addition, besides the featurerange provided by different machines, there is an often overlooked diversity inthe energy requirements introduced by hardware heterogeneity, which is simplyignored by default container orchestrator's placement strategies. We introduceHEATS, a new task-oriented and energy-aware orchestrator for containerizedapplications targeting heterogeneous clusters. HEATS allows customers to tradeperformance vs. energy requirements. Our system first learns the performanceand energy features of the physical hosts. Then, it monitors the execution oftasks on the hosts and opportunistically migrates them onto different clusternodes to match the customer-required deployment trade-offs. Our HEATS prototypeis implemented within Google's Kubernetes. The evaluation with synthetic tracesin 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.

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