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Traditionally, multi-tenant cloud and edge platforms use fair-share schedulers to fairly multiplex resources across applications. These schedulers ensure applications receive processing time proportional to a configurable share of the total time. Unfortunately, enforcing time-fairness across applications often violates energy-fairness, such that some applications consume more than their fair share of energy. This occurs because applications either do not fully utilize their resources or operate at a reduced frequency/voltage during their time-slice. The problem is particularly acute for machine learning (ML) applications using GPUs, where model size largely dictates utilization and energy usage. Enforcing energy-fairness is also important since energy is a costly and limited resource. For example, in cloud platforms, energy dominates operating costs and is limited by the power delivery infrastructure, while in edge platforms, energy is often scarce and limited by energy harvesting and battery constraints. To address the problem, we define the notion of Energy-Time Fairness (ETF), which enables a configurable tradeoff between energy and time fairness, and then design a scheduler that enforces it. We show that ETF satisfies many well-accepted fairness properties. ETF and the new tradeoff it offers are important, as some applications, especially ML models, are time/latency-sensitive and others are energy-sensitive. Thus, while enforcing pure energy-fairness starves time/latency-sensitive applications (of time) and enforcing pure time-fairness starves energy-sensitive applications (of energy), ETF is able to mind the gap between the two. We implement an ETF scheduler, and show that it improves fairness by up to 2x, incentivizes energy efficiency, and exposes a configurable knob to operate between energy- and time-fairness.
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Energy Time Fairness: Balancing Fair Allocation of Energy and Time for GPU Workloads
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Traditionally, multi-tenant cloud and edge platforms use fair-share schedulers to fairly multiplex resources across applications. These schedulers ensure applications receive processing time proportional to a configurable share of the total time. Unfortunately, enforcing time-fairness across applications often violates energy-fairness, such that some applications consume more than their fair share of energy. This occurs because applications either do not fully utilize their resources or operate at a reduced frequency/voltage during their time-slice. The problem is particularly acute for machine learning (ML) applications using GPUs, where model size largely dictates utilization and energy usage. Enforcing energy-fairness is also important since energy is a costly and limited resource. For example, in cloud platforms, energy dominates operating costs and is limited by the power delivery infrastructure, while in edge platforms, energy is often scarce and limited by energy harvesting and battery constraints. To address the problem, we define the notion of Energy-Time Fairness (ETF), which enables a configurable tradeoff between energy and time fairness, and then design a scheduler that enforces it. We show that ETF satisfies many well-accepted fairness properties. ETF and the new tradeoff it offers are important, as some applications, especially ML models, are time/latency-sensitive and others are energy-sensitive. Thus, while enforcing pure energy-fairness starves time/latency-sensitive applications (of time) and enforcing pure time-fairness starves energy-sensitive applications (of energy), ETF is able to mind the gap between the two. We implement an ETF scheduler, and show that it improves fairness by up to 2x, incentivizes energy efficiency, and exposes a configurable knob to operate between energy- and time-fairness.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3583740.3628435
