Multi-agent Reinforcement Learning Aided Service Function Chain Deployment for Internet of Things

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IEEE Internet of Things Journal


Nowadays, the compelling applications of the Internet of Things (IoT) bring unexpected economic benefits to our daily life. But at the same time, it also poses huge challenges to service providers. Diverse proprietary hardwares (i.e., firewall, code conversion) have to be deployed in networks for meeting different applications’ requirements. Recently, network functions virtualization (NFV) is considered a promising technique. In the NFV-enabled architecture, network services can be implemented via a set of orderly virtual network functions (VNFs) on standardized compute nodes, which is termed service function chains (SFCs). However, with the explosion of IoT applications, embedding multiple SFCs in a shared NFV-enabled infrastructure becomes a challenging problem. Centralized schemes suffer from the scalability and private issue, while distributed schemes suffer the non-convergence problem. In this paper, we propose a hybrid intelligent control architecture, which adopts the centralized training and distributed execution paradigm. A centralized critic is introduced to ease the training process of the distributed network nodes. Besides, considering the competitive behavior of users, we formulate the resource allocation problem as a multi-user competition game model. Based on this, we proposed a multi-agent reinforcement learning-based SFCs deployment algorithm.

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Costs, Games, Hardware, Internet of Things, Internet of Things, Multi-agent System, Network Function Virtualization, Privacy, Reinforcement Learning, Scalability, Servers, Service Function Chain.


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