Reinforcement Learning Applications for Performance Improvement in Cloud Computing—A Systematic Review

Lahande, Prathamesh Vijay; Kaveri, Parag Ravikant

doi:10.1007/978-981-16-9012-9_9

Lecture Notes in Electrical Engineering

2022

DOI: 10.1007/978-981-16-9012-9_9

|View full text |Cite

Reinforcement Learning Applications for Performance Improvement in Cloud Computing—A Systematic Review

Prathamesh Vijay Lahande

Parag Ravikant Kaveri

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Research trends in deep learning and machine learning for cloud computing security

Alzoubi,

Mishra,

Topcu

2024

Artif Intell Rev

View full text Add to dashboard Cite

Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles published in this field, there remains a dearth of comprehensive reviews that synthesize the techniques, trends, and challenges of using deep learning and machine learning for cloud computing security. Accordingly, this paper aims to provide the most updated statistics on the development and research in cloud computing security utilizing deep learning and machine learning. Up to the middle of December 2023, 4051 publications were identified after we searched the Scopus database. This paper highlights key trend solutions for cloud computing security utilizing machine learning and deep learning, such as anomaly detection, security automation, and emerging technology's role. However, challenges such as data privacy, scalability, and explainability, among others, are also identified as challenges of using machine learning and deep learning for cloud security. The findings of this paper reveal that deep learning and machine learning for cloud computing security are emerging research areas. Future research directions may include addressing these challenges when utilizing machine learning and deep learning for cloud security. Additionally, exploring the development of algorithms and techniques that comply with relevant laws and regulations is essential for effective implementation in this domain.

show abstract

Research trends in deep learning and machine learning for cloud computing security

Alzoubi,

Mishra,

Topcu

2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

A Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud Content Delivery Networks (CDNs)

Sasikumar,

Jayarin,

Vinnarasi

2023

J Cloud Comp

View full text Add to dashboard Cite

Content Delivery Networks (CDNs) have grown in popularity as a result of the ongoing development of the Internet and its applications. The workload on streaming media service systems can be significantly decreased with the help of the cooperative edge-cloud computing architecture. In the traditional works, a different types of content placement and routing algorithms are developed for improving the content delivery of cloud systems with reduced delay and cost. But, the majority of existing algorithms facing complexities in terms of increased resource usage, ineffective delivery, and high system designing complexity. Therefore, the proposed work aims to develop a new framework, named as, Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud CDNs. Here, the Chaotic Krill Herd Optimization (CKHO) method is used to optimize the resource usage for content placement. Then, a Hierarchical Probability Routing (HPR) model is employed to enable a dependable end-to-end data transmission with an optimized routing path. The performance of the proposed HORCP model is validated and compared by using several performance metrics. The obtained results are also compared with current state-of-the-art methodologies in order to show the superiority of the proposed HORCP model. By using the HORCP mechanism, the overall memory usage of the network is reduced to 80%, CPU usage is reduced to 20%, response is minimized to 2 s, and total congestion cost with respect to the network load level is reduced to 100.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Reinforcement Learning Applications for Performance Improvement in Cloud Computing—A Systematic Review

Cited by 2 publications

References 43 publications

Research trends in deep learning and machine learning for cloud computing security

Research trends in deep learning and machine learning for cloud computing security

A Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud Content Delivery Networks (CDNs)

Contact Info

Product

Resources

About