Leveraging Core and Uncore Frequency Scaling for Power-Efficient Serverless Workflows
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arXiv
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| Format: | Preprint |
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2024
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| author | Tzenetopoulos, Achilleas Masouros, Dimosthenis Xydis, Sotirios Soudris, Dimitrios |
| author_facet | Tzenetopoulos, Achilleas Masouros, Dimosthenis Xydis, Sotirios Soudris, Dimitrios |
| contents | 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 $Ω$kypous, an SLO-driven DVFS framework for serverless workflows. $Ω$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, $Ω$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18386 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Core and Uncore Frequency Scaling for Power-Efficient Serverless Workflows Tzenetopoulos, Achilleas Masouros, Dimosthenis Xydis, Sotirios Soudris, Dimitrios Distributed, Parallel, and Cluster Computing 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 $Ω$kypous, an SLO-driven DVFS framework for serverless workflows. $Ω$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, $Ω$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. |
| title | Leveraging Core and Uncore Frequency Scaling for Power-Efficient Serverless Workflows |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2407.18386 |