A Low|Overhead Online Workload Prediction Framework for Cloud ...

A Low-Overhead Online Workload Prediction Framework for Cloud ...

CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing. Abstract: Accurate prediction of future incoming workloads to cloud ...

A Low-Overhead Online Workload Prediction Framework for Cloud ...

CloudBruno: A Low-Overhead Online Workload Prediction. Framework for Cloud Computing. Vinodh Kumaran Jayakumar∗, Shivani Arbat†, In Kee Kim†, and Wei Wang ...

CloudBruno: A Low-Overhead Online Workload Prediction ...

This paper presents a generic and low-cost online workload prediction framework, called Cloud Bruno, which combines the more accurate LSTM models with less ...

CloudBruno: A Low-Overhead Online Workload Prediction ...

Hence, online retraining with expensive cloud CPUs/GPUs will increase cloud deployment costs. This work aims to build a cloud workload prediction framework with ...

A Low-Overhead Online Workload Prediction Framework for Cloud ...

Request PDF | On Sep 1, 2022, Vinodh Kumaran Jayakumar and others published CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud ...

A Low-Overhead Online Workload Prediction Framework for Cloud ...

TL;DR: A linear regression model is used to predict the workload and an auto-scaling mechanism is proposed to scale virtual resources at different resource ...

A Low-Overhead Online Workload Prediction Framework for Cloud ...

(DOI: 10.1109/ic2e55432.2022.00027) Accurate prediction of future incoming workloads to cloud applications, such as future user request ...

An Efficient Deep Learning Model to Predict Cloud Workload for ...

CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing · V. JayakumarShivani ArbatI. KimWei Wang. Computer Science, Engineering.

A Self-Optimized Generic Workload Prediction Framework for Cloud ...

We evaluated LoadDynamics with a mix- ture of workload traces representing public cloud applications, scientific applications, data center jobs and web ...

Advanced model for efficient workload prediction in the cloud

Usage of dynamically scalable and often virtualized computing resources that are available as services over the Internet have gained a lot of attention from ...

A Cloud Workloads Prediction Approach Integrating Short-Term and ...

... predicting the behavior of Internet of Things load, enabling ... Yu, “A long-term cloud workload prediction framework for reserved ...

A Long-term Cloud Workload Prediction Framework for Reserved ...

Download Citation | On Jul 1, 2022, Tianyang Wu and others published A Long-term Cloud Workload Prediction Framework for Reserved Resource Allocation | Find ...

Workload prediction in cloud using artificial neural network and ...

Homeostatic and tendency-based CPU load predictions. LiT.H.. A hierarchical framework for modeling and forecasting web server workload. J. Amer. Statist. Assoc ...

Wasserstein Adversarial Transformer for Cloud Workload Prediction

(Jayakumar et al. 2020) pro- posed LoadDynamics, a self-optimized generic workload prediction framework for cloud workloads. LoadDynam- ics performs autonomous ...

Automated Hyperparameter Tuning for Adaptive Cloud Workload ...

DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks. Engineering Applications of Artificial ...

Multivariate LSTM-Based Location-Aware Workload Prediction for ...

1 Excerpt. CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing · V. JayakumarShivani ArbatI. KimWei Wang. Computer Science ...

Workload Time Series Cumulative Prediction Mechanism for Cloud ...

In this paper, workload sequence prediction is treated as a translation problem. Therefore, an Attention Seq2Seq-based technique is proposed for predicting ...

Wasserstein Adversarial Transformer for Cloud Workload Prediction

This work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs.

A Self-decoupled Interpretable Prediction Framework for Highly ...

Cloud workloads prediction plays a crucial role in the various tasks of cloud computing, such as resource scheduling, ...

VTGAN: hybrid generative adversarial networks for cloud workload ...

[34] used a multiple linear regression (MLR) method to predict overutilized and underutilized servers. They integrated their prediction ...