Runtime data center temperature prediction using Grammatical Evolution techniques

• URL: http://arxiv.org/abs/2211.06329v1
• Date: Fri, 11 Nov 2022 16:30:26 GMT
• Title: Runtime data center temperature prediction using Grammatical Evolution techniques
• Authors: Marina Zapater, Jos\'e L. Risco-Mart\'in, Patricia Arroba, Jos\'e L. Ayala, Jos\'e M. Moya and Rom\'an Hermida
• Abstract summary: This paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2 C and 0.5 C in CPU and server inlet temperature respectively.
• Score: 1.8909283916360866
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2 C and 0.5 C in CPU and server inlet temperature respectively.

Related papers

• Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data [47.14384085714576]
  We introduce gridded pseudo-tokenPs to handle unstructured observations and a processor containing gridded pseudo-tokens that leverage efficient attention mechanisms. Our method consistently outperforms a range of strong baselines on various synthetic and real-world regression tasks involving large-scale data. The real-life experiments are performed on weather data, demonstrating the potential of our approach to bring performance and computational benefits when applied at scale in a weather modelling pipeline.
  arXiv  Detail & Related papers  (2024-10-09T10:00:56Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks [1.9116784879310036]
  Accurately predicting the temperature field in metal additive manufacturing processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability. We introduce a physics-informed neural network framework specifically designed for temperature field prediction in metal AM. We validate the proposed framework in two scenarios: full-field temperature prediction for a thin wall and 2D temperature field prediction for cylinder and cubic parts, demonstrating errors below 3% and 1%, respectively.
  arXiv  Detail & Related papers  (2024-01-04T18:42:28Z)
• Machine Learning Force Fields with Data Cost Aware Training [94.78998399180519]
  Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels. We propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
  arXiv  Detail & Related papers  (2023-06-05T04:34:54Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• Physics-based Digital Twins for Autonomous Thermal Food Processing: Efficient, Non-intrusive Reduced-order Modeling [0.0]
  This paper proposes a physics-based, data-driven Digital Twin framework for autonomous food processing. A correlation between a high standard deviation of the surface temperatures in the training data and a low root mean square error in ROM testing enables efficient selection of training data.
  arXiv  Detail & Related papers  (2022-09-07T10:58:38Z)
• Sample-dependent Adaptive Temperature Scaling for Improved Calibration [95.7477042886242]
  Post-hoc approach to compensate for neural networks being wrong is to perform temperature scaling. We propose to predict a different temperature value for each input, allowing us to adjust the mismatch between confidence and accuracy. We test our method on the ResNet50 and WideResNet28-10 architectures using the CIFAR10/100 and Tiny-ImageNet datasets.
  arXiv  Detail & Related papers  (2022-07-13T14:13:49Z)
• Tensor Network for Supervised Learning at Finite Temperature [1.4717465036484292]
  finite temperature tensor network (FTTN) imports thermal perturbation into matrix product states framework. FTTN regards it as thermal loss computed from entanglement with environment. Temperature-like parameter can be automatically optimized, which gives each database an individual temperature.
  arXiv  Detail & Related papers  (2021-04-09T05:02:36Z)
• Statistical Post-processing for Gridded Temperature Forecasts Using Encoder-Decoder Based Deep Convolutional Neural Networks [0.0]
  Japan Meteorological Agency (JMA) has been operating gridded temperature guidance for predicting snow amount and precipitation type. It has been difficult to correct a temperature field when NWP models did not predict the location of a front correctly or when the observed temperature was extremely cold or hot. In this paper, encoder-decoder-based convolutional neural networks (CNNs) were employed to predict temperatures at the surface around the Kanto district.
  arXiv  Detail & Related papers  (2021-03-02T05:28:31Z)
• Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning [31.735983199708013]
  We study data from a private cloud and show the presence of thermal variations. We propose a gradient boosting machine learning model for temperature prediction. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts.
  arXiv  Detail & Related papers  (2020-11-07T00:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.