Datacenter Outage Detection Using ML Models
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Solution Overview
Problem
As datacenters incorporate more devices, processing resources, and applications, efficiently identifying the source of a datacenter outage becomes increasingly difficult, leading to prolonged downtime and decreased user experience.
Innovation Solution
The use of machine learning models that analyze near real-time and offline data to detect datacenter mass outages, processing data from various sources such as server temperatures, power usage, and ticketing data to identify anomalous parameters and project the sources of the outage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If more devices and applications are implemented in a datacenter, then the datacenter's processing capability and functionality are improved, but the difficulty of identifying the source of an outage increases
Solution Approach 1:
The patent segments the complex datacenter monitoring problem into multiple independent analysis components: collecting data from diverse sources (servers, network devices, applications), analyzing each data type separately using appropriate methods, and then integrating results to identify outage sources. This segmentation makes the overall system manageable despite the increasing complexity of datacenter environments.
Solution Approach 2:
The patent introduces an intermediary analysis system that sits between the raw data from multiple datacenter sources and the final outage identification. This intermediary layer collects, processes, and correlates data from various sources using multiple analysis techniques, thereby mediating the complexity between diverse data sources and the need for clear outage source identification.
2Device complexity
If traditional monitoring methods are used to identify outage sources, then system simplicity is maintained, but the time to detect and resolve outages increases
Solution Approach 1:
The patent merges multiple analysis techniques (statistical analysis, machine learning, rule-based analysis) and multiple data sources into a single integrated monitoring system. This combination enables comprehensive outage detection and source identification that is both fast and accurate, overcoming the limitations of traditional single-method approaches while managing complexity through unified architecture.
Solution Approach 2:
The patent performs preliminary actions by continuously collecting and pre-processing data from all datacenter sources before outages occur. Historical data is stored and analyzed in advance, establishing baselines and patterns that enable rapid outage detection and source identification when anomalies occur, thereby reducing overall resolution time.
3Measurement precision
If comprehensive data collection from multiple sources is performed, then the accuracy of outage source identification is improved, but the processing complexity and computational resources required increase
Solution Approach 1:
The patent segments the comprehensive data processing task into distinct processing pipelines for different data types (server metrics, network data, application logs). Each segment is processed using specialized techniques appropriate to its format and characteristics, improving accuracy while managing complexity through modular processing architecture.
Solution Approach 2:
The patent transforms raw data from multiple sources into standardized parameters and features that can be uniformly analyzed. By changing the parameter representation of diverse data types into common analytical formats, the system achieves high identification accuracy while simplifying the processing complexity through parameter standardization.
Data Source
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AI summary
The present embodiments relate to data center outage detection and alert generation. An outage detection service as described herein can process near real-time data from various sources in a datacenter and process the data using a model to determine one or more projected sources of a detected outage. The model as described herein can include one or more machine learning models incorporating a series of rules to process near-real time data and offline data and determine one or more projected sources of an outage. An alert message can be generated to provide the projected sources of the outage and other data relevant to the outage.