Root-Cause Diagnosis in Virtualized Data Networks
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Solution Overview
Problem
The increased use of virtual machines in data networks complicates root-cause problem diagnosis due to shared resources with physical systems and dynamic re-location, making it difficult to assess and diagnose issues effectively.
Innovation Solution
A method that associates metrics with network components, obtains and correlates indicative problems based on interconnections and interdependencies between physical and virtual machines, using a hierarchical layer model to rank and diagnose issues, and prioritizes problems for effective root-cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machines are used to share physical resources, then resource utilization efficiency is improved, but problem diagnosis complexity increases
Solution Approach 1:
The patent segments the diagnosis process into distinct layers (physical infrastructure layer, virtualization layer, virtual machine layer, application layer) and associates specific metrics with each layer. This segmentation allows the system to handle the complexity of virtualized environments by breaking down the diagnostic task into manageable, layer-specific components rather than attempting to analyze the entire system simultaneously.
Solution Approach 2:
The patent introduces an intermediary monitoring system that sits between the physical infrastructure and the virtual machines. This intermediary collects metrics from multiple layers, correlates them according to defined relationships, and presents integrated diagnostic information. The intermediary handles the complexity of mapping between physical and virtual components, shielding users from the underlying diagnostic complexity while maintaining high resource utilization.
2Adaptability or versatility
If virtual machines are dynamically relocated between physical systems, then system flexibility and load balancing are improved, but tracking and diagnosing problems becomes more difficult
Solution Approach 1:
The patent creates a universal monitoring framework that can track both physical and virtual components across multiple systems. The system uses unique identifiers and maintains correlation mappings that work regardless of which physical host a virtual machine is running on. This universal approach allows the same diagnostic methodology to be applied whether VMs are static or dynamically relocated, preserving tracking information across migrations.
Solution Approach 2:
The patent implements continuous feedback mechanisms where the monitoring system regularly updates its knowledge of virtual machine locations and status. By continuously collecting metrics and updating correlation information, the system maintains current tracking data even as VMs are dynamically relocated. This feedback loop ensures that diagnostic information remains accurate and up-to-date despite system changes.
3Measurement precision
If comprehensive metrics are collected from all layers, then diagnosis accuracy is improved, but data processing and correlation complexity increases
Solution Approach 1:
The patent segments metrics into layer-specific categories (physical infrastructure metrics, virtualization layer metrics, virtual machine metrics, application metrics) and defines specific correlation rules for each layer. This segmentation allows the system to process comprehensive data by handling each layer's metrics separately according to its specific characteristics, then integrating the results through defined correlation relationships rather than processing all data uniformly.
Solution Approach 2:
The patent applies different processing qualities and methods to different layers based on their specific needs. Each layer has tailored metric collection, processing, and correlation rules appropriate to its characteristics. For example, physical infrastructure metrics may be processed differently from virtual machine metrics, allowing optimized processing for each layer while maintaining overall diagnostic accuracy.
Data Source
AI summary
An improved root-cause approach to problem diagnosis in data networks in the form of a method comprising the steps of: associating each metric in a at least one set of metrics with at least one component and/or network device; obtaining values for each such metric from a monitoring system; determining whether each such metric is indicative of a problem within the data network; and ranking and correlating indicative problems to determine whether a problem may be symptomatic of another problem based on an interconnection and/or interdependency between a physical machine and a virtual machine, between components or between components and network devices.