微电网模拟校准用于能源感知计算系统
Calibrating Microgrid Simulations for Energy-Aware Computing Systems
AI总结：
本文提出一种自校准的能源感知软件测试平台，通过整合可再生能源生产模拟器和真实计算节点，提升计算节点的能耗预测精度，验证了Kepler框架在GPU和CPU负载下的准确性。
摘要：
计算资源需求的激增正在增加数据中心的全球电力消耗，预计到2026年将超过1000 TWh，主要归因于新型AI技术的采用。碳感知计算策略可通过将电力消耗与低碳可再生能源生产对齐来减轻环境影响，但受限于开发环境的稀缺性。现有解决方案要么依赖于成本高且复杂的物理系统架构，难以集成和维护，要么依赖于全模拟，虽然更经济，但往往缺乏现实性，因忽略了系统开销、实时节点电力消耗和资源波动。本论文通过提出一种自校准的能源感知软件测试平台，利用Software-in-the-Loop协同模拟框架Vessim整合可再生能源生产模拟器，同时包含真实计算节点。这些节点的应用层电力消耗首先通过Kepler框架进行近似，然后在Vessim的微电网模拟中通过外部插座功率计进行校准。对GPU和CPU密集型工作负载的测试表明，Kepler框架对整个计算节点的电力近似具有相当的准确性，平均回归系数为1.01，R²值为0.95，尽管某些机器学习工作负载显示了更高的偏差。平均静态回归线的截距约为5.23 W，表明空闲功率近似的不准确性。动态每进程电力消耗的校准使GPU工作负载的准确性提高了约50%，而CPU工作负载的准确性则有所提升约3.5%。
英文摘要：
The surge for computing resource demand is increasing global electricity consumption in data centers which is expected to exceed 1000 TWh by 2026, mainly attributable to adoption of new AI technologies. Carbon-aware computing strategies can mitigate their environmental impact by aligning power consumption with the production of low-carbon renewable energy, but they face challenges due to the scarcity of development environments. Existing solutions either rely on costly and complex physical system architectures that are difficult to integrate and maintain or on full simulations that, while more economical, often lack realism by ignoring system overheads, and real-time node power consumption and resource fluctuations. This thesis remediates these issues by proposing a self-calibrating energy-aware software testbed that uses the Software-in-the-Loop co-simulation framework Vessim to integrate renewable energy production simulators, while including real computing nodes. The application-level power consumption of these are first approximated by the Kepler framework and then calibrated within Vessim's microgrid simulation using an external socket power meter as a definitive measurement source on the system-level. The evaluation of the testbed with GPU and CPU intensive workloads reveal fairly accurate power approximation of the whole computing node by the Kepler framework, with an average regression coefficient of 1.01 and R^2 values of 0.95, though certain machine learning workloads showed higher deviation. The average static y-intercept of the regression line of ~5.23 W indicate inaccuracies in the idle power approximation. Calibration of dynamic per-process power consumption improved accuracy for GPU workloads by ~50%, while CPU workloads saw a modest improvement of ~3.5%.
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