Results 101 to 110 of about 181,670 (124)

Predicting CPU Usage for Proactive Autoscaling

Proceedings of the 1st Workshop on Machine Learning and Systems, 2021
Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application's run-time requirements, but only help the cloud infrastructure resource manager to map requested resources to physical resources.
Marco Chiesa
exaly   +3 more sources

Proactive Autoscaling for Cloud-Native Applications Using Machine Learning

GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020
Cloud computing and cloud-native applications have become standards for new developments in most organizations. Many companies are moving their workload toward microservices and profit from the cloud-native paradigm. However, there are still many challenges to overcome to optimize the Quality of Service using autoscaling and resource dimensioning ...
Nicolas Marie-Magdelaine, Toufik Ahmed
exaly   +3 more sources

OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud. [PDF]

CoRR, 2023
Autoscaling is a vital mechanism in cloud computing that supports the autonomous adjustment of computing resources under dynamic workloads. A primary goal of autoscaling is to stabilize resource utilization at a desirable level, thus reconciling the need
Ding Zou, Wei Lu 0030, Zhibo Zhu, Xingyu Lu, Jun Zhou, Xiaojin Wang, Kangyu Liu, Haiqing Wang, Kefan Wang, Renen Sun   +9 more
openaire   +2 more sources

Reactive vs. Proactive Autoscaling in Kubernetes

Studies in Autonomic, Data-driven and Industrial Computing
Pradeepika Verma, Anshul Verma, Bablu Kumar   +2 more
exaly   +2 more sources

Optimizing Resource Allocation in Cloud-Native Applications Through Proactive Autoscaling With the InformerAutoScale Model

Journal of Supercomputing
Akram Bennour, Anshul Verma, Bablu Kumar
exaly   +3 more sources

AI-Based Proactive Autoscaling Technique for Web Applications in Kubernetes

2026 IEEE 18th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)
Halyna Beshley, Mykola Beshley
exaly   +2 more sources

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Journal of Grid Computing
Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up ...
Pintye, I., Lovas, R., Kovacs, J.
exaly   +2 more sources

A Proactive Q-Learning Approach for Autoscaling Heterogeneous Cloud Servers

2018 14th European Dependable Computing Conference (EDCC), 2018
Cloud providers offer different physical or virtual machine (VM) types that have different computational power and cost. Choosing the right configuration in a such heterogeneous environment able to sustain a workload while minimising costs is a challenging key aspect.
openaire   +1 more source

Proactive Horizontal Pod Autoscaling in Kubernetes using Bi-LSTM

2023 IEEE International Conference on Contemporary Computing and Communications (InC4), 2023
Soham Kakade, Gurutej Abbigeri, Om Prabhu, Akash Dalwayi, Narayan G, Somashekar Patil Patil, Bhagya Sunag   +6 more
openaire   +1 more source

Proactive Resource Autoscaling Scheme Based on SCINet for High-Performance Cloud Computing

IEEE Transactions on Cloud Computing, 2023
Byeonghui Jeong, Jueun Jeon, Young-Sik Jeong   +2 more
openaire   +2 more sources