K-means and alternative clustering methods in modern power systems

SM Miraftabzadeh, CG Colombo, M Longo… - IEEE …, 2023 - ieeexplore.ieee.org
As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

A taxonomy and survey of power models and power modeling for cloud servers

W Lin, F Shi, W Wu, K Li, G Wu… - ACM Computing Surveys …, 2020 - dl.acm.org
Due to the increasing demand of cloud resources, the ever-increasing number and scale of
cloud data centers make their massive power consumption a prominent issue today …

Clustering cloud workloads: K-means vs gaussian mixture model

E Patel, DS Kushwaha - Procedia computer science, 2020 - Elsevier
The growing heterogeneity due to diverse Cloud workloads such as Big Data, IoT and
Business Data analytics, requires precise characterization to design a successful capacity …

Power modeling for energy-efficient resource management in a cloud data center

A Tarafdar, S Sarkar, RK Das, S Khatua - Journal of Grid Computing, 2023 - Springer
An accurate host power model is necessary for effective power management in data centers
which is crucial for reducing energy consumption and cost. One should evaluate the power …

An artificial neural network approach to power consumption model construction for servers in cloud data centers

W Lin, G Wu, X Wang, K Li - IEEE Transactions on Sustainable …, 2019 - ieeexplore.ieee.org
The power consumption estimation or prediction of cloud servers is the basis of energy-
aware scheduling to realize energy saving in cloud datacenters. The existing works are …

A recent review of risk-based inspection development to support service excellence in the oil and gas industry: an artificial intelligence perspective

T Aditiyawarman, APS Kaban… - … -ASME Journal of …, 2023 - asmedigitalcollection.asme.org
Inspection and Maintenance methods development have a pivotal role in preventing the
uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating …

A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers

W Lin, T Yu, C Gao, F Liu, T Li, S Fong, Y Wang - Information Sciences, 2021 - Elsevier
The energy consumption of cloud servers accounts for about 25% of the total energy of
cloud data centers. Reducing and optimizing this energy consumption is thus extremely …

Combining genetic algorithms and bayesian neural networks for resource usage prediction in multi-tenant container environments

S Park, H Bahn - Cluster Computing, 2025 - Springer
Traditional cloud architectures struggle to effectively allocate resources to container-based
workloads due to fluctuating usage patterns and potential interference among multi-tenants …

ARIMA time Series Model vs. K-means clustering for cloud workloads performance

VK Mishra, M Mishra, S Tekale… - 2022 OPJU …, 2023 - ieeexplore.ieee.org
To develop a thriving ability plan and protect the observable of Internet service providers of
cloud environment, the increased heterogeneity brought on by various Cloud workloads …

Comparison of Clustering Results using K-Means, Gaussian Mixture Models, Based on Seven Sectors of Country Electricity and Correlation with Gross National …

S Lukas, RM Suryaputri… - 2024 2nd International …, 2024 - ieeexplore.ieee.org
Choosing the optimal method for clustering is important. In this research, clustering was
carried out against 117 countries based on seven electricity sector indices, using data on 3 …