An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
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 workloads, cloud service providers can automatically provision and deprovision user resources for the system beforeh...
Main Authors: | Nhat-Minh Dang-Quang, Myungsik Yoo |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-03-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/12/7/3523 |
Similar Items
-
Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
by: Nhat-Minh Dang-Quang, et al.
Published: (2021-04-01) -
Predictive Hybrid Autoscaling for Containerized Applications
by: Dinh-Dai Vu, et al.
Published: (2022-01-01) -
Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes
by: Subrota Kumar Mondal, et al.
Published: (2023-06-01) -
An Autoscaling System Based on Predicting the Demand for Resources and Responding to Failure in Forecasting
by: Jieun Park, et al.
Published: (2023-11-01) -
Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration
by: Thanh-Tung Nguyen, et al.
Published: (2020-08-01)