LossLeaP: Learning to Predict for Intent-Based Networking

Abstract

Intent-Based Networking mandates that high-level human-understandable intents are automatically interpreted and implemented by network management entities. As a key part in this process, it is required that network orchestrators activate the correct automated decision model to meet the intent objective. In anticipatory networking tasks, this requirement maps to identifying and deploying a tailored prediction model that can produce a forecast aligned with the specific -and typically complex-network management goal expressed by the original intent. Current forecasting models for network demands or network management optimize generic, non-flexible, and manually designed objectives, hence do not fulfil the needs of anticipatory Intent-Based Networking. To close this gap, we propose LossLeaP, a novel forecasting model that can autonomously learn the relationship between the prediction and the target management objective, steering the former to minimize the latter. To this end, LossLeaP adopts an original deep learning architecture that advances current efforts in automated machine learning, towards a spontaneous design of loss functions for regression tasks. Extensive experiments in controlled environments and in practical application case studies prove that LossLeaP outperforms a wide range of benchmarks, including state-of-the-art solutions for network capacity forecasting.

Explainable and Transferable Loss Meta-Learning for Zero-Touch Anticipatory Network Management

“…We also remark that early versions of this study have appeared in subsequent editions of IEEE INFOCOM [17], [18]: the present manuscript represents a comprehensive treatise of the proposed loss meta-learning strategy for network management, and includes a range of original evaluations of the capabilities of the AutoManager model.…”

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

“…The concept is introducing loss functions that are designed by network experts and are tailored to the downstream application: such custom functions can train the predictor model to produce an output that is aligned with the requirements of the end user. Loss meta-learning frameworks such as that by Collet et al [124] also start being proposed, which automate the definition of the most appropriate loss function to the target performance objective prediction.…”

NLP Powered Intent Based Network Management for Private 5G Networks

“…6 depicts the interactions generated by the RNS model. In the third type of intent in Table 1 compute related information is provided as a list of target network service identifiers (NSIds) that will make use of the deployed slice 3 . The CR module uses the list of NSIds to dimension the CPUs, memory and storage requirements of the slice, through the interactions depicted in Fig.…”

“…The main concept behind intent based networking is adopting declarative interfaces where network operators specify what they want to achieve, instead of specifying how to achieve it. In this regard, Machine Learning (ML) is seen as a key enabler to autonomously fill the missing context required to translate high level intents into low level network configurations [3]. A relevant example of the configuration complexity incurred in private 5G networks is provided in [4], where the authors introduce 5G-CLARITY, which is a novel architecture for 5G private networks integrating 5GNR, Wi-Fi and Li-Fi access networks.…”