Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments

Jalodia, Nikita and Henna, Shagufta and Davy, Alan (2019) Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments. In: IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2019 - Proceedings :. IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2019 - Proceedings . Institute of Electrical and Electronics Engineers Inc., USA. ISBN 9781728145457

Full text not available from this repository. (Request a copy)

Abstract

Network Function Virtualisation (NFV) has emerged as a key paradigm in network softwarisation, enabling virtualisation in future generation networks. Once deployed, the Virtual Network Functions (VNFs) in an NFV application's Service Function Chain (SFC) experience dynamic fluctuations in network traffic and requests, which necessitates dynamic scaling of resource instances. Dynamic resource management is a critical challenge in virtualised environments, specifically while balancing the trade-off between efficiency and reliability. Since provisioning of virtual infrastructures is time-consuming, this negates the Quality of Service (QoS) requirements and reliability criterion in latency-critical applications such as autonomous driving. This calls for predictive scaling decisions to balance the provisioning time sink, with a methodology that preserves the topological dependencies between the nodes in an SFC for effective resource forecasting. To address this, we propose the model for an Asynchronous Deep Reinforcement Learning (DRL) enhanced Graph Neural Networks (GNN) for topology-aware VNF resource prediction in dynamic NFV environments.

Item Type:	Book Section
Additional Information:	Funding Information: This work has been funded by Science Foundation Ireland (SFI) and the European Regional Development Fund under Grant Number 13/RC/2077. Publisher Copyright: © 2019 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1705
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:16
Last Modified:	10 Aug 2023 04:15
URI:	http://repository-testing.wit.ie/id/eprint/5057

Actions (login required)

View Item