Outage Forecasting via Composite Loss Functions
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Solution Overview
Problem
Conventional outage forecasting systems in computer networks are unable to accurately predict service outages due to reliance on pre-labeled training data sets and simple, inaccurate loss functions, which limits their ability to handle extreme events and requires re-training when outage thresholds change.
Innovation Solution
A machine learning model trained using a distribution loss and a classification loss to generate probability distribution information for service metrics, allowing for accurate prediction of outages without relying on pre-labeled data and enabling flexible threshold settings, thereby predicting rare and extreme outages effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional outage forecasting systems use simple loss functions and pre-labeled training data, then the system is easier to implement, but the prediction accuracy for extreme events deteriorates
Solution Approach 1:
The patent changes the parameter of the loss function from simple accuracy-based metrics to a composite loss function that incorporates both classification loss and distribution loss. This allows the model to capture both discrete outage events and continuous probability distributions, significantly improving prediction accuracy for extreme events while maintaining a structured training approach.
Solution Approach 2:
The patent segments the training data into labeled outage events and unlabeled normal operation data. By processing these segments differently through separate loss functions (classification loss for labeled data, distribution loss for unlabeled data), the system achieves high accuracy without requiring complete pre-labeling of all training data.
2Ease of manufacture
If conventional systems rely on pre-labeled training data sets, then the training process is simpler, but the ability to handle extreme events and adapt to threshold changes deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability by designing a loss function that can handle both labeled and unlabeled data flexibly. The distribution loss component allows the model to adapt to arbitrary threshold changes without retraining, as it learns the underlying probability distribution rather than fixed threshold boundaries. This dynamic approach maintains training simplicity while dramatically improving adaptability.
Solution Approach 2:
The composite loss function serves multiple functions simultaneously: it handles both labeled outage detection and unlabeled distribution learning, and adapts to different threshold settings. This multi-functional design eliminates the need for separate models or extensive retraining when thresholds change, achieving both simplicity and versatility.
3Loss of time
If conventional outage forecasting systems use simple loss functions, then the training time is reduced, but the accuracy in predicting rare and extreme outages deteriorates
Solution Approach 1:
The distribution loss component enables continuous learning from unlabeled data during normal operation, allowing the model to continuously refine its understanding of the service metric distribution. This continuous useful action during both training and inference improves prediction accuracy for rare events without requiring extensive additional training time, as the learning occurs incrementally.
Data Source
AI summary
A method and system for outage forecasting are described. One or more aspects of the method and system include receiving, by a machine learning model, time series data for a service metric of a computer network; generating, by the machine learning model, probability distribution information for the service metric based on the time series data, wherein the probability distribution information is generated using a machine learning model that is trained using a distribution loss and a classification loss; and generating, by a forecasting component, outage forecasting information for the computer network based on the probability distribution information.