Causality Analysis for Cloud Auto-Scaling
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Solution Overview
Problem
Conventional monitoring techniques fail to capture causality relationships between metrics at different layers in Cloud environments, leading to sub-optimal scalability and auto-configuration of IT solutions, as they do not account for business-level performance indicators and diverse architectures and metrics across solutions.
Innovation Solution
A causality analysis method using a multi-level causality mapping to aggregate metrics across infrastructure, platform, and application layers, employing logical operations and structural equation models to assess unobservable higher-level solution states, enabling effective auto-scaling and auto-configuration based on business performance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring techniques are used to monitor compute, network, storage and task execution, then infrastructure scalability can be achieved, but causality relationships between metrics at different layers cannot be captured
Solution Approach 1:
The patent introduces a causality analysis layer as an intermediary between traditional monitoring systems and scalability decision-making. This layer includes components such as a causality graph builder, metric correlation analyzer, and business indicator mapper that actively capture and analyze causality relationships between metrics across infrastructure, platform, and application layers, preventing loss of critical causal information
Solution Approach 2:
The patent adds a new dimensional layer of analysis by introducing business-level performance indicators and causality relationships as additional dimensions beyond traditional infrastructure metrics. This multi-dimensional approach enables comprehensive scalability decisions that consider both technical metrics and business impact
2Loss of information
If multi-level causality mapping is implemented to capture causality relationships, then business level performance indicators can be captured, but system complexity increases
Solution Approach 1:
The patent segments the complex monitoring and analysis system into distinct modular components: infrastructure layer monitors, platform layer monitors, application layer monitors, causality graph builder, metric correlation analyzer, and business indicator mapper. Each component handles specific tasks independently, making the overall complex system manageable and maintainable
Solution Approach 2:
The causality graph serves as an intermediary data structure that organizes and represents complex causality relationships in a structured format. This graph includes nodes representing metrics and edges representing causal relationships, enabling the system to manage complex causality information systematically
3Adaptability or versatility
If conventional monitoring is used, then infrastructure metrics can be monitored, but diverse architectures and metrics for different solutions cannot be accommodated
Solution Approach 1:
The patent creates a universal monitoring framework that can accommodate diverse architectures and metrics across different solutions. The system uses standardized interfaces and abstractions that allow it to monitor various infrastructure, platform, and application metrics regardless of the specific architecture or solution type, while maintaining comprehensive monitoring coverage
Data Source
AI summary
Similar to other Cloud Service, Solution as Services over Cloud, as single tenant technology, also requires support of agility and flexibility as a fundamental feature of Cloud computing. Different from other Cloud services, the agility and flexibility typically are not triggered by the typical performance metrics, but at the business level of metrics. A causality analysis method, system, and non-transitory computer readable medium using a causal graph depicting relationships among observable primitive metrics from infrastructure, middleware, and business metrics and latent business metrics of an application, include identifying a metric value resulting from measuring the system and application metrics, determining an impact of the measurement of the metrics on the business metrics associated with the measurable metrics in the causal graph, and determining an action to take with respect to the impact on the business metric based on the pre-defined business policies.