Cloud Power Meter: Bridging the Gap in Power Measurement for Heterogeneous Cloud

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Abstract
The significance of accurate power measurement in cloud computing can be attributed to its increasing usability as well as for attaining energy savings through optimization and advancing green computing in diverse cloud platforms like AWS, GCP, Chameleon Cloud, and Microsoft Azure. The challenge of providing a single yet accurate methodology, suitable for the cloud environment, is exacerbated by factors like numerous heterogeneity of the cloud, unavailability of administrative access required by precise methods like Intel's Running Average Power Limit (RAPL) and the inaccuracy of widely-accepted methods like Thermal Design Power (TDP) based estimation. In this paper, we propose Cloud Power Meter (CPM) in pursuit of sustainability in cloud operations, which integrates a decision tree with multi-variable polynomial regression. Being an ML-based method that is trained using diverse cloud instances from Amazon, Microsoft, and Google, CPM is capable of providing precise power estimation on the cloud platforms without administrative privileges. The decision tree guarantees performance capacity across heterogeneous cloud environments by locating the “CPM Instance” - the closest match of instances that are not familiar to CPM. In evaluation through the Standard Performance Evaluation Corporation (SPEC) benchmark's CPU power consumption, CPM outperforms 14 existing power models and its power estimation matches Intel's RAPL power model. To evaluate the effectiveness of CPM, we conduct an extensive analysis of approximately 2.6 million real-world Virtual Machines (VMs) running on Microsoft Azure. CPM successfully identifies opportunities to save over 80,000 kilowatt-hours (kWh) of energy in a single month through optimized resource allocation. Notably, about 25,000 kWh of this energy is being wasted by more than 17% of the VMs. Further, CPM's energy estimations reveal that 366 VMs (out of roughly 2.6 million) wasted nearly 15,000 kWh of energy despite never exceeding 2% CPU utilization.
Funding Information
  • Department of Computer Science, Texas State University

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