Hybrid auto-scaled service-cloud-based predictive workload modeling and analysis for smart campus system

MA Razzaq, JA Mahar, M Ahmad, N Saher… - IEEE …, 2021 - ieeexplore.ieee.org
The Internet of Things is an emerging technology used in cloud computing and provides
many services of the cloud. The cloud services users mostly suffer from service delays and …

The 3-axis scalable service-cloud resource modeling for burst prediction under smart campus scenario

MA Razzaq, JA Mahar, M Ahmad, I Ali… - IEEE …, 2021 - ieeexplore.ieee.org
Internet of Things (IoT) enables smart campuses more convenient for cloud services. The
availability of cloud resources to its users appears as a fundamental challenge. The existing …

Machine Learning based Workload Prediction for Auto-scaling Cloud Applications

ST Singh, M Tiwari, AS Dhar - 2022 OPJU International …, 2023 - ieeexplore.ieee.org
Cloud computing is a ubiquitous computing paradigm that offers its users access to software,
platforms, and infrastructure as services, on-demand, over the Internet. User requests for …

Burst-aware predictive autoscaling for containerized microservices

M Abdullah, W Iqbal, JL Berral, J Polo… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Autoscaling methods are used for cloud-hosted applications to dynamically scale the
allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application …

System resource utilization analysis and prediction for cloud based applications under bursty workloads

J Yin, X Lu, H Chen, X Zhao, NN Xiong - Information Sciences, 2014 - Elsevier
Performance analysis and prediction need a solid understanding of the system workload. As
a salient workload characteristic, burstiness has critical impact on resource provisioning and …

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 …

Online workload burst detection for efficient predictive autoscaling of applications

F Tahir, M Abdullah, F Bukhari, KM Almustafa… - IEEE …, 2020 - ieeexplore.ieee.org
Autoscaling methods are employed to ensure the scalability of cloud-hosted applications.
The public-facing applications are prone to receive sudden workload bursts, and the existing …

Proactive workload management in hybrid cloud computing

H Zhang, G Jiang, K Yoshihira… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
The hindrances to the adoption of public cloud computing services include service reliability,
data security and privacy, regulation compliant requirements, and so on. To address those …

Analytical modeling and prediction of cloud workload

T Daradkeh, A Agarwal, M Zaman… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
Cloud workload prediction is a very critical task for elastic scaling, because cloud manager
decides what configuration sequence is to be considered for resource provisioning …

A survey of the workload forecasting methods in cloud computing

A Yadav, S Kushwaha, J Gupta, D Saxena… - Proceedings of 3rd …, 2022 - Springer
A few years ago, cloud computing had widely altered the way of computation and storage. It
is quite challenging for cloud service providers to maintain the required quality of service …