Explainability Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network

Terra, Ahmad; Inam, Rafia; Baskaran, Sandhya; Batista, Paula; Burdick, Ian; Fersman, Elena

doi:10.1109/globecom42002.2020.9322496

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…In future work, we hope to investigate the potential use of XAI in other telecommunication domains, such as security, anomaly detection, resource allocation etc., as well as any additional benefits that XAI can offer within these settings, such as detecting distributional shifts in the input data [14] or performing root-cause analysis [11].…”

Section: Discussionmentioning

confidence: 99%

“…Within the general slicing literature, only limited research pertains to the use of XAI; in [10], the authors outline the need for explainability in future 6G networks and provide a brief overview of how they envision XAI can be applied to handover and resource allocation problems. In [11], the authors compare the use of various XAI methods on a service-level agreement (SLA) violation model. Their results found that the SHapley Additive eXplanation (SHAP) method [12] provides the most consistent results when cross-referenced against a causal analysis tool.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Barnard¹,

Macaluso²,

Marchetti³

et al. 2021

Preprint

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The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Barnard¹,

Macaluso²,

Marchetti³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We evaluate the performance of our model using a series of key performance indicator (KPI) metrics, such as the root mean-square error (RMSE), and over/under reservation, as defined by equations ( 10) and (11), respectively, where t ∈ T refers to samples taken from the test set and 1{•} denotes the indicator function. For comparison, we also show the performance of a naive solution, which always reserves the most recent traffic demand for the next scheduling window.…”

Section: A Performance Of Strr Modelmentioning

confidence: 99%

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Barnard

Macaluso

Marchetti

et al. 2022

ICC 2022 - IEEE International Conference on Communications

View full text Add to dashboard Cite

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-theart techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model's general behaviour, as well as aid in the diagnosis of potential faults during the model's development.In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

show abstract

“…Ref. [26] investigates the root-cause of Service Level Agreements (SLA) violation prediction for 5G network slicing while leveraging different XAI frameworks, such as SHAP, LIME, and Eli5 to enhance trust in system and improve performance. Specifically, Ref.…”

Section: B Survey On Xai For Networkingmentioning

confidence: 99%

“…Specifically, Ref. [26] sets up an emulated 5G core network in order to collect field data, such as latency measurements, and applies an Extreme Gradient Boosting (XGBoost) model to predict latency violations. XAI frameworks are applied to validate and explain the cause of SLA violation predictions.…”

Section: B Survey On Xai For Networkingmentioning

confidence: 99%

Towards explainable artificial intelligence in optical networks: the use case of lightpath QoT estimation

Ayoub

Troia

Andreoletti

et al. 2022

J. Opt. Commun. Netw.

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Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial capabilities in solving a wide range of optical-network-related tasks such as fault management, resource allocation, and lightpath quality of transmission (QoT) estimation. However, the focus of the research community has been centered on ML models’ predictive capabilities, neglecting aspects related to models’ understanding, i.e., to interpret how the model reasons and makes its predictions. This lack of transparency hinders the understanding of a model’s behavior and prevents operators from judging, and hence trusting, the model’s decisions. To mitigate the lack of transparency and trust in ML, explainable AI (XAI) frameworks can be leveraged to explain how a model correlates input features to its outputs. In this paper, we focus on the application of XAI to lightpath QoT estimation. In particular, we exploit Shapley additive explanations (SHAP) as the XAI framework. Before presenting our analysis, we provide a brief overview of XAI and SHAP, then discuss the benefits of the application of XAI in networking and survey studies that apply XAI to networking tasks. Then, we model the lightpath QoT estimation problem as a supervised binary classification task to predict whether the value of the bit error rate associated with a lightpath is below or above a reference acceptability threshold and train an ML extreme gradient boosting model as the classifier. Finally, we demonstrate how to apply SHAP to extract insights about the model and to inspect misclassifications.

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Explainability Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network

Cited by 24 publications

References 16 publications

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Towards explainable artificial intelligence in optical networks: the use case of lightpath QoT estimation

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