Policy Network Assisted Monte Carlo Tree Search for Intelligent Service Function Chain Deployment

Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisti...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE ... International Conference on Trust, Security and Privacy in Computing and Communications (Online) s. 1161 - 1168
Hlavní autoři: Fu, Zhihan, Fan, Qilin, Zhang, Xu, Li, Xiuhua, Wang, Sen, Wang, Yueyang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2021
Témata:
Monte Carlo methods
Monte Carlo Tree Search
Network security
Privacy
Reinforcement learning
Search problems
Service Function Chain
Service function chaining
Software
Virtual Network Function
ISSN:2324-9013
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisting of a series of ordered virtual network security functions is becoming a mainstream form to carry network security services. Allocating the underlying physical network resources to the demands of SFCs under given constraints over time is known as the SFC deployment problem. It is a crucial issue for infrastructure providers. However, SFC deployment is facing new challenges in trading off between pursuing the objective of a high revenue-to-cost ratio and making decisions in an online manner. In this paper, we investigate the use of reinforcement learning to guide online deployment decisions for SFC requests and propose a Policy network Assisted Monte Carlo Tree search approach named PACT to address the above challenge, aiming to maximize the average revenue-to-cost ratio. PACT combines the strengths of the policy network, which evaluates the placement potential of physical servers, and the Monte Carlo Tree Search, which is able to tackle problems with large state spaces. Extensive experimental results demonstrate that our PACT achieves the best performance and is superior to other algorithms by up to 30% and 23.8% on average revenue-to-cost ratio and acceptance rate, respectively.
ISSN:2324-9013
DOI:10.1109/TrustCom53373.2021.00157