Multi-Agent Deep Reinforcement Learning for Weighted Multi-Path Routing

Θεοδωρόπουλος, Θεόδωρος; Kafetzis, Dimitrios; Violos, John; Makris, Antonios; Tserpes, Konstantinos

doi:10.1145/3589010.3594888

Proceedings of the 3rd Workshop on Flexible Resource and Application Management on the Edge 2023

DOI: 10.1145/3589010.3594888

|View full text |Cite

Multi-Agent Deep Reinforcement Learning for Weighted Multi-Path Routing

Θεόδωρος Θεοδωρόπουλος

Dimitrios Kafetzis

John Violos

et al.

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 Cite1

Independent1

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The control plane is responsible for the overall orchestration of sidecar behavior. Herein, the proxies provide the capabilities to configure the components in order to collect telemetry and enable observability and also to apply policies for routing traffic [96]. The service mesh concept is used as an architectural approach to enforce security policies on top of microservices network traffic.…”

mentioning

confidence: 99%

Security in Cloud-Native Services: A Survey

Theodoropoulos,

Rosa,

Benzaid

et al. 2023

JCP

Self Cite

View full text Add to dashboard Cite

Cloud-native services face unique cybersecurity challenges due to their distributed infrastructure. They are susceptible to various threats like malware, DDoS attacks, and Man-in-the-Middle (MITM) attacks. Additionally, these services often process sensitive data that must be protected from unauthorized access. On top of that, the dynamic and scalable nature of cloud-native services makes it difficult to maintain consistent security, as deploying new instances and infrastructure introduces new vulnerabilities. To address these challenges, efficient security solutions are needed to mitigate potential threats while aligning with the characteristics of cloud-native services. Despite the abundance of works focusing on security aspects in the cloud, there has been a notable lack of research that is focused on the security of cloud-native services. To address this gap, this work is the first survey that is dedicated to exploring security in cloud-native services. This work aims to provide a comprehensive investigation of the aspects, features, and solutions that are associated with security in cloud-native services. It serves as a uniquely structured mapping study that maps the key aspects to the corresponding features, and these features to numerous contemporary solutions. Furthermore, it includes the identification of various candidate open-source technologies that are capable of supporting the realization of each explored solution. Finally, it showcases how these solutions can work together in order to establish each corresponding feature. The insights and findings of this work can be used by cybersecurity professionals, such as developers and researchers, to enhance the security of cloud-native services.

show abstract

mentioning

confidence: 99%

Security in Cloud-Native Services: A Survey

Theodoropoulos,

Rosa,

Benzaid

et al. 2023

JCP

Self Cite

View full text Add to dashboard Cite

show abstract

Towards establishing intelligent multi-domain edge orchestration for highly distributed immersive services: a virtual touring use case

Benmerar,

Theodoropoulos,

Fevereiro

et al. 2024

Cluster Comput

View full text Add to dashboard Cite

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

hi@scite.ai

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

Henderson, NV 89052, USA

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.

Multi-Agent Deep Reinforcement Learning for Weighted Multi-Path Routing

Cited by 2 publications

References 18 publications

Security in Cloud-Native Services: A Survey

Security in Cloud-Native Services: A Survey

Towards establishing intelligent multi-domain edge orchestration for highly distributed immersive services: a virtual touring use case

Contact Info

Product

Resources

About