2024/07/25 by Achilleas Tzenetopoulos, Dimosthenis Masouros, Tzenetopoulos, Achilleas +5
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2407.18386
openalex publication_date 2024/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 Ωkypous, an SLO-driven DVFS framework for serverless workflows. Ωkypous 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, Ωkypous 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.