Anomaly Detection System for Web Server Failure Prediction
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Solution Overview
Problem
Predicting web server failures in cloud-based applications is challenging due to varying loads and types of applications, which can lead to catastrophic failures if not addressed promptly, affecting the availability of software-as-a-service (SAAS) to end-users.
Innovation Solution
An anomaly detection system utilizing a combination of machine learning and statistical technologies, including a machine learning model and a forecaster, processes real-time data from web servers to predict failure states, aggregating outputs to reduce errors and provide timely alerts for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detection method is used to predict web server failures, then the detection speed is fast, but the accuracy is low leading to false positives and negatives
Solution Approach 1:
The detection system is segmented into two independent processing chains: a machine learning-based chain for pattern recognition and a forecaster-based chain for statistical prediction. Each chain operates independently with its own algorithms and evaluation metrics, then their results are combined through aggregation to achieve higher overall accuracy than either chain alone.
Solution Approach 2:
The system uses a composite detection approach by combining outputs from two different detection methodologies (machine learning models and statistical forecasters) into a unified failure state determination. This composite structure leverages the strengths of both approaches while mitigating their individual weaknesses.
2Reliability
If multiple detection methods are combined to improve accuracy, then the detection accuracy improves, but the system complexity increases
Solution Approach 1:
The system maintains reliability while managing complexity through clear segmentation into two processing chains with distinct responsibilities. The machine learning chain handles complex pattern recognition, while the forecaster chain handles statistical predictions, reducing the cognitive and computational complexity of managing the entire system.
Solution Approach 2:
An aggregation mechanism serves as an intermediary that combines the outputs of the two processing chains. This mediator component simplifies the integration process by providing a standardized interface for combining results, managing the complexity of coordinating multiple detection methods while maintaining high reliability.
3Loss of time
If real-time data processing is implemented to enable timely failure detection, then the response time is reduced, but the computational load increases
Solution Approach 1:
The system applies partial action by having each processing chain focus on specific aspects of failure detection rather than all chains analyzing all data types. The machine learning chain processes certain features while the forecaster chain processes others, reducing the total computational load while maintaining comprehensive coverage for timely detection.
Data Source
AI summary
In some embodiments, there may be provided a system. The system may be configured to receive web server data indicating a current state of a web server; determine, by a machine learning model and based on the web server data, whether the web server is in a first failure state; generate a first failure state indication for the web server in response to the determination, by the machine learning model, that the web server is in the first failure state; determine, by a forecaster and based on the web server data, whether the web server is in a second failure state; and generate a second failure state indication for the web server in response to the determination, by the forecaster, that the web server is in the second failure state.