Machine learning-based auto-scaling for containerized applications
M Imdoukh, I Ahmad, MG Alfailakawi - Neural Computing and Applications, 2020 - Springer
Containers are shaping the new era of cloud applications due to their key benefits such as
lightweight, very quick to launch, consuming minimum resources to run an application which …
lightweight, very quick to launch, consuming minimum resources to run an application which …
[HTML][HTML] Deep learning-based autoscaling using bidirectional long short-term memory for kubernetes
NM Dang-Quang, M Yoo - Applied Sciences, 2021 - mdpi.com
Presently, the cloud computing environment attracts many application developers to deploy
their web applications on cloud data centers. Kubernetes, a well-known container …
their web applications on cloud data centers. Kubernetes, a well-known container …
Why is it not solved yet? challenges for production-ready autoscaling
M Straesser, J Grohmann, J von Kistowski… - Proceedings of the …, 2022 - dl.acm.org
Autoscaling is a task of major importance in the cloud computing domain as it directly affects
both operating costs and customer experience. Although there has been active research in …
both operating costs and customer experience. Although there has been active research in …
K-agrued: A container autoscaling technique for cloud-based web applications in kubernetes using attention-based gru encoder-decoder
J Dogani, F Khunjush, M Seydali - Journal of Grid Computing, 2022 - Springer
Cloud service providers can operate several execution instances on a single physical server
using virtualization technology, which improves resource utilization. In recent years …
using virtualization technology, which improves resource utilization. In recent years …
[HTML][HTML] An efficient multivariate autoscaling framework using Bi-lstm for cloud computing
NM Dang-Quang, M Yoo - Applied Sciences, 2022 - mdpi.com
With the rapid development of 5G technology, the need for a flexible and scalable real-time
system for data processing has become increasingly important. By predicting future resource …
system for data processing has become increasingly important. By predicting future resource …
[HTML][HTML] Performance-cost trade-off in auto-scaling mechanisms for cloud computing
Cloud computing has been widely adopted over the years by practitioners and companies
with a variety of requirements. With a strong economic appeal, cloud computing makes …
with a variety of requirements. With a strong economic appeal, cloud computing makes …
Quantifying cloud performance and dependability: Taxonomy, metric design, and emerging challenges
In only a decade, cloud computing has emerged from a pursuit for a service-driven
information and communication technology (ICT), becoming a significant fraction of the ICT …
information and communication technology (ICT), becoming a significant fraction of the ICT …
Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model
J Dogani, F Khunjush - Journal of Parallel and Distributed Computing, 2024 - Elsevier
Edge computing has emerged as an attractive alternative to traditional cloud computing by
utilizing processing, network, and storage resources close to end devices, such as Internet …
utilizing processing, network, and storage resources close to end devices, such as Internet …
Online machine learning for auto-scaling in the edge computing
TP da Silva, AR Neto, TV Batista, FC Delicato… - Pervasive and Mobile …, 2022 - Elsevier
The evolution of edge computing devices has enabled machine intelligence techniques to
process data close to its producers (the sensors) and end-users. Although edge devices are …
process data close to its producers (the sensors) and end-users. Although edge devices are …
SARDE: a framework for continuous and self-adaptive resource demand estimation
Resource demands are crucial parameters for modeling and predicting the performance of
software systems. Currently, resource demand estimators are usually executed once for …
software systems. Currently, resource demand estimators are usually executed once for …