AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges, and future perspectives

GK Walia, M Kumar, SS Gill - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several
domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to …

Machine learning (ML)-centric resource management in cloud computing: A review and future directions

T Khan, W Tian, G Zhou, S Ilager, M Gong… - Journal of Network and …, 2022 - Elsevier
Cloud computing has rapidly emerged as a model for delivering Internet-based utility
computing services. Infrastructure as a Service (IaaS) is one of the most important and …

Machine learning based workload prediction in cloud computing

J Gao, H Wang, H Shen - 2020 29th international conference …, 2020 - ieeexplore.ieee.org
As a widely used IT service, more and more companies shift their services to cloud
datacenters. It is important for cloud service providers (CSPs) to provide cloud service …

Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms

E Cortez, A Bonde, A Muzio, M Russinovich… - Proceedings of the 26th …, 2017 - dl.acm.org
Cloud research to date has lacked data on the characteristics of the production virtual
machine (VM) workloads of large cloud providers. A thorough understanding of these …

[HTML][HTML] Prediction of home energy consumption based on gradient boosting regression tree

P Nie, M Roccotelli, MP Fanti, Z Ming, Z Li - Energy Reports, 2021 - Elsevier
Energy consumption prediction of buildings has drawn attention in the related literature
since it is very complex and affected by various factors. Hence, a challenging work is …

Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey

TL Duc, RG Leiva, P Casari, PO Östberg - ACM Computing Surveys …, 2019 - dl.acm.org
Large-scale software systems are currently designed as distributed entities and deployed in
cloud data centers. To overcome the limitations inherent to this type of deployment …

From cloud to edge: a first look at public edge platforms

M Xu, Z Fu, X Ma, L Zhang, Y Li, F Qian… - Proceedings of the 21st …, 2021 - dl.acm.org
Public edge platforms have drawn increasing attention from both academia and industry. In
this study, we perform a first-of-its-kind measurement study on a leading public edge …

esDNN: deep neural network based multivariate workload prediction in cloud computing environments

M Xu, C Song, H Wu, SS Gill, K Ye, C Xu - ACM Transactions on Internet …, 2022 - dl.acm.org
Cloud computing has been regarded as a successful paradigm for IT industry by providing
benefits for both service providers and customers. In spite of the advantages, cloud …

[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges

S Tuli, F Mirhakimi, S Pallewatta, S Zawad… - Journal of Network and …, 2023 - Elsevier
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

BHyPreC: a novel Bi-LSTM based hybrid recurrent neural network model to predict the CPU workload of cloud virtual machine

ME Karim, MMS Maswood, S Das, AG Alharbi - IEEE Access, 2021 - ieeexplore.ieee.org
With the advancement of cloud computing technologies, there is an ever-increasing demand
for the maximum utilization of cloud resources. It increases the computing power …