A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions

Abstract

In this paper, we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the final goal of having greener and more sustainable data centers. Modern enterprises and agile ways-of-working have led to a complete revolution of the way that software engineers develop and deploy software, with the proliferation of container-based technology, such as Kubernetes and Docker. Modern systems tend to use up a large amount of resources, even when idle, and intelligent scaling is one of the methods that could be used to prevent waste. We have developed a system for predicting and influencing computer resource usage based on historical data of real production software systems at CERN, allowing us to scale down the number of machines or containers running a certain service during periods that have been identified as idle. The system leverages recurring neural network models in order to accurately predict the future usage of a software system given its past activity. Using the data obtained from conducting several experiments with the forecasted data, we present the potential reductions on the carbon footprint of these computing services, from the perspective of CPU usage. The results show significant improvements to the computing power usage of the service (60% to 80%) as opposed to just keeping machines running or using simple heuristics that do not look too far into the past.

Recurrent Neural Network (RNN) Inference Cloud Computing

“…To minimize losses from oversupply and conserve energy, a deep learning-based method for optimizing computer resource utilization is presented in the research paper [34] authors Marius Cioca and Ioan Cristian Schuszter and others (2022) by projecting future resource requirements, this method makes sure computers use less electricity and last longer. In order to guarantee that management efforts are not in vain and that computer resources are used sustainably, it also recommends the use of renewable resources.…”

An operation site security detection method based on point cloud data and improved YOLO algorithm under the architecture of the power internet of things

“…However, deep learning algorithms, with their excellent feature expression ability and learning advantages, can effectively achieve target recognition [8]. Therefore, applying deep learning algorithms to safety control of power operation sites has practical significance, but the research on deep learning in the field of safety detection is still shallow, and the detection reliability needs to be further improved.…”

Power cable monitoring method based on UHF‐RFID and deep learning in edge computing environment

“…In recent years, with the development and application of deep learning, certain research results have been achieved in the field of cable temperature prediction. However, most methods have problems such as complex models, low prediction efficiency, and unsatisfactory accuracy [9]. Therefore, a power cable monitoring method based on UHF‐RFID and deep learning in the edge computing environment is proposed.…”