Predictive Anomaly Detection for Multi-Tier Service Failure Isolation
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Solution Overview
Problem
As computing environments grow in size and complexity, identifying and addressing anomalous behavior and application impairments in distributed multi-tiered computing ecosystems becomes crucial to maintain performance and meet service level objectives.
Innovation Solution
Implementing a management hierarchy with global, domain, and device level controllers to monitor application SLO metrics, perform anomaly detection, and execute remediation and root cause analysis to mitigate service failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anomaly detection and remediation systems are implemented in distributed multi-tiered computing environments, then service reliability and performance are improved, but system complexity increases
Solution Approach 1:
The system divides the distributed computing environment into multiple tiers (edge tier, core tier, cloud tier) with dedicated controllers for each level. Each tier has specialized anomaly detection and remediation capabilities, allowing complex systems to be managed through modular, hierarchical segments rather than monolithic complexity.
Solution Approach 2:
Controllers at each tier act as intermediaries between lower and higher tiers. Edge controllers manage local devices, core controllers coordinate between edge and cloud tiers, and cloud controllers provide overall orchestration. This intermediary structure simplifies communication and management across the complex distributed system.
2Measurement precision
If comprehensive monitoring and root cause analysis are performed, then anomaly detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary anomaly detection at the edge tier using local controllers before issues propagate upward. Root cause analysis is initiated proactively by detecting anomalies in service level objectives (SLOs) and metrics before they manifest as failures, enabling early intervention and reducing overall response time.
Solution Approach 2:
The system analyzes anomalies across multiple dimensions simultaneously - temporal patterns, spatial distribution across tiers, dependency relationships between services, and metric correlations. This multi-dimensional approach improves detection accuracy without linearly increasing processing time by leveraging parallel analysis across different dimensions.
3Reliability
If service level objective metrics are monitored across all tiers, then service performance management improves, but data volume and storage requirements increase
Solution Approach 1:
The system extracts and processes only the critical SLO metrics and key performance indicators at each tier rather than monitoring all possible data points. Edge controllers extract relevant local metrics, core controllers aggregate essential intermediate metrics, and cloud controllers store only high-level summary statistics, significantly reducing data volume while maintaining effective performance management.
Solution Approach 2:
The system transforms raw monitoring data into normalized SLO metric parameters with standardized thresholds and aggregation rules. By changing the representation of data from raw measurements to normalized service-level parameters, the system reduces data volume and simplifies storage requirements while improving performance management effectiveness.
Data Source
AI summary
Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, service level objective (SLO) metrics; applying the SLO metrics to a predictive anomaly detection transformer to perform anomaly detection; making a first determination that an anomaly is detected; in response to the first determination: attempting basic remediation to resolve the anomaly; making a second determination that the basic remediation is unsuccessful; in response to the second determination: making a third determination that the anomaly is associated with a silent failure; and in response to the third determination: performing service impairment isolation to obtain a collection of services correlated to the anomaly; and performing root cause analysis to identify causal services.