AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach

H Sami, H Otrok, J Bentahar… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Currently, researchers have motivated a vision of 6G for empowering the new generation of
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …

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 …

Performance analysis of machine learning centered workload prediction models for cloud

D Saxena, J Kumar, AK Singh… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The precise estimation of resource usage is a complex and challenging issue due to the
high variability and dimensionality of heterogeneous service types and dynamic workloads …

Resource allocation with workload-time windows for cloud-based software services: a deep reinforcement learning approach

X Chen, L Yang, Z Chen, G Min… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As the workloads and service requests in cloud computing environments change constantly,
cloud-based software services need to adaptively allocate resources for ensuring the Quality …

[HTML][HTML] Workflow performance prediction based on graph structure aware deep attention neural network

J Yu, M Gao, Y Li, Z Zhang, WH Ip, KL Yung - Journal of Industrial …, 2022 - Elsevier
With the rapid growth of cloud computing, efficient operational optimization and resource
scheduling of complex cloud business processes rely on real-time and accurate …

FAST: A forecasting model with adaptive sliding window and time locality integration for dynamic cloud workloads

B Feng, Z Ding, C Jiang - IEEE Transactions on Services …, 2022 - ieeexplore.ieee.org
The workload predictor has attracted attention as a key component of the proactive service
operation management framework. However, the request and resource workloads of cloud …

PSO-based ensemble meta-learning approach for cloud virtual machine resource usage prediction

HL Leka, Z Fengli, AT Kenea, NW Hundera, TG Tohye… - Symmetry, 2023 - mdpi.com
To meet the increasing demand for its services, a cloud system should make optimum use of
its available resources. Additionally, the high and low oscillations in cloud workload are …

Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives

B Feng, Z Ding - Tsinghua Science and Technology, 2024 - ieeexplore.ieee.org
Workload prediction is critical in enabling proactive resource management of cloud
applications. Accurate workload prediction is valuable for cloud users and providers as it …

Coin: a container workload prediction model focusing on common and individual changes in workloads

Z Ding, B Feng, C Jiang - IEEE Transactions on Parallel and …, 2022 - ieeexplore.ieee.org
Recently, containers have become the primary deployment form for cloud applications.
Predicting container workload accurately is critical to ensure the quality of service (QoS) and …

Arima-based and multiapplication workload prediction with wavelet decomposition and savitzky–golay filter in clouds

J Bi, H Yuan, S Li, K Zhang, J Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Current cloud data centers (CDCs) provide highly scalable, flexible, and cost-effective
services to meet the performance needs of emerging applications. It is critical for CDC …