A Network Intelligence Architecture for Efficient VNF Lifecycle Management

Lange, Stanislav; Tú, Nguyễn Văn; Jeong, Seyeon; Lee, Do-Young; Kim, Heegon; Jiang, Hong; Yoo, Jae‐Hyoung; Hong, James Won‐Ki

doi:10.1109/tnsm.2020.3015244

Cited by 24 publications

(23 citation statements)

References 37 publications

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“…Pei et al 23 proposed a deep‐learning‐based two‐phase algorithm that relies on VNF selection and VNF chaining networks designed to achieve both efficient VNF selection and chaining for dynamic SFC deployment. Lange et al 24 proposed a real‐time VNF migration algorithm based on the deep belief network (DBN) that predicts future VNFs' resource demands. In the proactive mechanisms, the VNF instances' scaling is executed in advance to mitigate the influences of the deployment delay on network services.…”

Section: Related Workmentioning

confidence: 99%

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Cai

Qian

Luo

et al. 2022

Int J of Intelligent Sys

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Network function virtualization is a promising technology for providing personalized services via agile service function chains (SFCs). Flexible SFC orchestration and rational resource allocation are pivotal for improving the SFC's quality of service (QoS). However, the requirements for computational load and resources have frequently been changing. Consequently, static resource allocation can result in resource insufficiency when SFCs turn busy and resource waste due to resource overplus when SFCs are idle. Since a dynamic resource allocation is necessary, the existing dynamic resource allocation methods' responses have often been delayed. This paper proposes an SFC active reconfiguration mechanism (SARM) based on computational load and resource demand. The SARM predicts nodes' computation loads and SFCs' resource demands and uses these predictions to estimate future QoS and develop the SFC reconfiguration strategy. The SARM considers multiple factors and applies a heuristic algorithm to achieve the tradeoff between migration cost and QoS preservation. The experiments demonstrate that the SARM can effectively predict the nodes' load and the resource demand of SFCs. In addition, the SARM can successfully identify the SFCs to reconfigure and reduce the QoS maintenance costs. The simulation results indicate that the average delays of the SFCs can be reduced by at least 26%.

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Section: Related Workmentioning

confidence: 99%

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Cai

Qian

Luo

et al. 2022

Int J of Intelligent Sys

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“…Notably, machine learning (ML) based schemes have been shown to provide a promising solution to realize these functionalities (e.g., VNF scaling [21]). Nonetheless, the majority of these efforts do not provide a holistic approach that can aid the VNFM in selecting, at any given point in time, nor given a measured behavior of the VNF, the most appropriate VNF reconfiguration action among all possible LCM operations [22]. Furthermore, most VNFs are VM-based, which wastes a lot of computing resources on OS instantiating in each VM instance.…”

Section: Motivationmentioning

confidence: 99%

“…To manage the VNF properly and efficiently, a VNFM should include traditional fault management configuration management, performance management and security management(FCAPS) as well as the lifecycle management [22] including operations such as:…”

Section: Vnf Lifecycle Managementmentioning

confidence: 99%

“…The work in [56] presents a PAMDP on solving 5 G-related problems using reinforcement learning. Finally, Lange et al [57] provide a comprehensive analysis of existing ML-related VNF management approaches and then propose a general network intelligence architecture for VNF that focuses on using ML for several separate functionalities such as anomaly detection, VNF placement, and chaining.…”

Section: Current Vnf Lcm Problem Solutionsmentioning

confidence: 99%

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An Automated VNF Manager based on Parameterized Action MDP and Reinforcement Learning

Samaan

Karmouch

2021

ICC 2021 - IEEE International Conference on Communications

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Managing and orchestrating the behaviour of virtualized Network Functions (VNFs) remains a major challenge due to their heterogeneity and the ever increasing resource demands of the served flows. In this thesis, we propose a novel VNF manager (VNFM) that employs a parameterized actions-based reinforcement learning mechanism to simultaneously decide on the optimal VNF management action (e.g., migration, scaling, termination or rebooting) and the action's corresponding configuration parameters (e.g., migration location or amount of resources needed for scaling ). More precisely, we first propose a novel parameterized-action Markov decision process (PAMDP) model to accurately describe each VNF, instances of its components and their communication as well as the set of permissible management actions by the VNFM and the rewards of realizing these actions. The use of parameterized actions allows us to rigorously represent the functionalities of the VNFM in order perform various Lifecycle management (LCM) operations on the VNFs. Next, we propose a two-stage reinforcement learning (RL) scheme that alternates between learning an action-value function for the discrete LCM actions and updating the actions parameters selection policy. In contrast to existing machine learning schemes, the proposed work uniquely provides a holistic management platform the unifies individual efforts targeting individual LCM functions such as VNF placement and scaling. Performance evaluation results demonstrate the efficiency of the proposed VNFM in maintaining the required performance level of the VNF while optimizing its resource configurations. xii VM Virtual Machine

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“…There are also no previous studies on which parameters most influence the consumption of virtualized resources at different load levels. Although there are references to VNF management [15,16,17,18], there are no specific works dealing with NN when it affects 5G core functions. And finally, while there are some suggestions for algorithms that identify NN or predict VNFs performance, they have not been described as part of a 5G production environment.…”

Section: Introductionmentioning

confidence: 99%

Noisy Neighbour Impact Assessment and Prevention in Virtualized Mobile Networks

Muro¹,

Baena²,

Fortes³

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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The generalization in the use of virtualization in the upcoming generation of cellular networks involves new paradigms and approaches for their management. The correct sharing of the underlying resources between multiple virtualized network functions as well as any other processes sharing the same computational platform implies a complex architecture that makes virtualization challenging. As a result, numerous variables (e.g., computational capacity) were mostly ignored by previous management systems, but that can now lead to relevant impacts on the service performance. In this virtualized scenario with multiple coexistent processes over the same hardware, a "Noisy Neighbour" (NN) is identified as an entity that uses most of the underlying resources while other virtual units suffer a lack of them. While difficult to identify, such situations can affect the network service. In this context, the present work analyzes and assesses the NN problem for 5G Core scenarios. A complete emulated 5G network and analysis framework is defined and developed to evaluate the impact of a noisy entity. In this way, the degradation that Key Performance Indicators suffer in the network and by the end-users when a NN appears is assessed. Thus, the present work proposes a baseline for handling NN through a novel lifecycle management flow. For this purpose, it is evaluated the effectiveness of multiple Machine Learning (ML) models for the identification of NN, based on the metrics gathered from the proposed framework, achieving 99% of accuracy. Moreover, ML is applied to develop a method for network performance inference, along with a prediction model to forecast the number of CPU resources the network may demand at any given time supporting the proposed management flow.

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A Network Intelligence Architecture for Efficient VNF Lifecycle Management

Cited by 24 publications

References 37 publications

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

An Automated VNF Manager based on Parameterized Action MDP and Reinforcement Learning

Noisy Neighbour Impact Assessment and Prevention in Virtualized Mobile Networks

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