Leveraging Core and Uncore Frequency Scaling for Power-Efficient Serverless Workflows

TL;DR

This paper presents $\ extOmega$kypous, a novel DVFS framework that dynamically adjusts processor frequencies to reduce power consumption in serverless workflows while ensuring latency SLOs are met.

Contribution

It introduces a grey-box predictive model and a closed-loop control system for power-efficient frequency scaling in serverless workflows, addressing latency and power trade-offs.

Findings

Achieves 16% average power reduction on real Azure traces.

Maintains low SLO violation rate of 1.8%.

Outperforms existing power management frameworks.

Abstract

Serverless workflows have emerged in Function-as-a-Service (FaaS) platforms to represent the operational structure of traditional applications. With latency propagation effects becoming increasingly prominent, step-wise resource tuning is required to address Service-Level-Objectives (SLOs). Modern processors' allowance for fine-grained Dynamic Voltage and Frequency Scaling (DVFS), coupled with serverless workflows' intermittent nature, presents a unique opportunity to reduce power while meeting SLOs. We introduce $\Omega$kypous, an SLO-driven DVFS framework for serverless workflows. $\Omega$kypous employs a grey-box model that predicts functions' execution latency and power under different Core and Uncore frequency combinations. Based on these predictions and the timing slacks between workflow functions, $\Omega$kypous uses a closed-loop control mechanism to dynamically adjust Core and Uncore frequencies, thus minimizing power consumption without compromising predefined end-to-end latency constraints. Our evaluation on real-world traces from Azure, against state-of-the-art power management frameworks, demonstrates an average power consumption reduction of 16\%, while consistently maintaining low SLO violation rates (1.8\%), when operating under power caps.