External Memories of PDP Switches for In-Network Implementable Functions Placement: Deep Learning Based Reconfiguration of SFCs

Somayeh Kianpisheh; Tarik Taleb

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arXiv · 2409.08043

External Memories of PDP Switches for In-Network Implementable Functions Placement: Deep Learning Based Reconfiguration of SFCs

Somayeh Kianpisheh·Tarik Taleb

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Abstract

Network function virtualization leverages programmable data plane switches to deploy in-network implementable functions, to improve QoS. The memories of switches can be extended through remote direct memory access to access external memories. This paper exploits the switches external memories to place VNFs at time intervals with ultra-low latency and high bandwidth demands. The reconfiguration decision is modeled as an optimization to minimize the deployment and reconfiguration cost, while meeting the SFCs deadlines. A DRL based method is proposed to reconfigure service chains adoptable with dynamic network and traffic characteristics. To deal with slow convergence due to the complexity of deployment scenarios, static and dynamic filters are used in policy networks construction to diminish unfeasible placement exploration. Results illustrate improvement in convergence, acceptance ratio and cost.

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BibTeX RIS

Somayeh Kianpisheh, Tarik Taleb. 2024-09-12. External Memories of PDP Switches for In-Network Implementable Functions Placement: Deep Learning Based Reconfiguration of SFCs. https://arxiv.org/abs/2409.08043

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