ENNP: an enhanced neural networks-based power consumption prediction algorithm to deep learning-based workloads on AI-enabled data centers

The traditional power consumption prediction algorithms are no longer suitable for deep learning-based workloads owing to the long-term dependencies and highly volatile nature of power usage in such applications. To address these challenges, this study proposes ENNP, an enhanced neural network-based...

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Vydáno v:	Cluster computing Ročník 29; číslo 1; s. 36
Hlavní autoři:	Wang, Hengdong, Jing, Chao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.02.2026 Springer Nature B.V
Témata:	Accuracy Algorithms Anomalies Bayesian analysis Computer centers Computer Communication Networks Computer Science Data centers Deep learning Machine learning Natural language processing Neural networks Operating Systems Optimization Power consumption Prediction models Processor Architectures Time series Workload Workloads Power consumption prediction KAN AI-enabled data centers TimeMixer LSTM Bayesian optimization
ISSN:	1386-7857, 1573-7543
On-line přístup:	Získat plný text
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