Multi-Temporal Database for Cloud Fault Detection
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Solution Overview
Problem
Current cloud system operations face challenges in managing and monitoring millions of hardware and software components due to labor-intensive reactive fault detection and manual diagnosis techniques, which are often too late in responsiveness and unable to scale properly, requiring continuous measurement of vital signs and predictive anomaly detection to prevent SLA violations.
Innovation Solution
A data-driven, situation-aware computing system that uses multi-temporal databases to collect and process high-value data, enabling real-time data processing and automatic diagnosis through filter queries and data transformation processes, combining knowledge and process management with near-real-time frameworks to detect anomalies and invoke corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reactive fault detection and manual diagnosis techniques are used, then domain expertise can be applied to diagnose faults, but the system responsiveness is too late and labor intensity is high
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing operational data from multiple sources (logs, metrics, traces) before faults actually occur. This enables predictive anomaly detection that identifies potential issues in advance, allowing proactive remediation before SLA violations happen, thus improving responsiveness while maintaining accurate fault detection
Solution Approach 2:
The system implements self-service through automated anomaly detection and diagnosis capabilities that do not require manual domain expertise for every incident. The unified data model and automated analysis engine enable the system to self-diagnose issues across heterogeneous cloud infrastructure, reducing both labor intensity and response time simultaneously
2Measurement precision
If manual diagnosis techniques are used, then detailed analysis can be performed, but the system cannot scale properly for cloud environments
Solution Approach 1:
The system replaces manual mechanical diagnosis processes with automated computational analysis. The unified data model and automated anomaly detection engine process vast amounts of operational data from millions of cloud components without human intervention, maintaining high diagnosis accuracy while enabling the system to scale to cloud-level complexities that would be impossible for manual processes
Solution Approach 2:
The unified data model serves multiple functions simultaneously - it normalizes data from diverse sources (logs, metrics, traces), enables cross-source correlation, supports predictive analytics, and provides a common framework for automated diagnosis. This multi-functionality allows the system to maintain precise diagnosis across heterogeneous cloud infrastructure while scaling to handle massive volumes of data from numerous components
3Reliability
If continuous monitoring of key performance metrics is implemented, then SLA violations can be predicted, but data processing complexity increases
Solution Approach 1:
The system extracts only the most critical and relevant features from vast amounts of operational data using the unified data model. By focusing on key performance indicators and anomalies that directly impact SLA conformance rather than processing all raw data, the system maintains high reliability for predicting SLA violations while reducing overall data processing complexity through selective extraction of meaningful signals
Data Source
AI summary
Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.