Storage Network Traffic Load Analysis via Hierarchical Correlation
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Solution Overview
Problem
Large and complex storage area networks (SANs) face challenges in managing data traffic loads due to their size, heterogeneity, and frequent changes, leading to inefficiencies and high risks of failures, as existing monitoring approaches are resource-specific and point-in-time oriented, failing to consider end-to-end service levels and resource capacity effectively.
Innovation Solution
The system periodically analyzes and stores data traffic loads across SAN components, correlating resource types and logical access paths to compute hierarchical traffic load distributions, identify deviations, and provide notification messages, enabling better resource planning and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource-specific and point-in-time monitoring approaches are used, then implementation simplicity is maintained, but the ability to consider end-to-end service levels and resource capacity is insufficient
Solution Approach 1:
The patent segments the monitoring system into multiple functional modules: a collection module that gathers traffic load data from various sources, a processing module that analyzes the collected data, and a presentation module that displays results. This segmentation allows the system to achieve comprehensive end-to-end monitoring while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent implements a universal monitoring framework that can monitor multiple types of resources (storage devices, networks, applications) through a single integrated system. The system collects, processes, and presents traffic load information across the entire storage network environment, enabling end-to-end service level monitoring without requiring separate specialized tools for each resource type.
2Reliability
If comprehensive end-to-end traffic load analysis is implemented, then service level management is improved, but the complexity of correlating information and analyzing access paths increases
Solution Approach 1:
The patent introduces an intermediary processing module that acts as a mediator between data collection and analysis. This module receives traffic load information from multiple sources, correlates the data with access path information, and processes the combined data before presentation. The intermediary simplifies the correlation process by centralizing the integration logic in a dedicated component rather than distributing complexity across the entire system.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing the relationships between access paths, storage devices, and traffic loads. The system collects and stores access path information in advance, so that when traffic load data is collected, the correlation can be performed more efficiently by matching against pre-stored access path definitions rather than computing relationships in real-time.
3Reliability
If frequent monitoring intervals are used, then traffic load imbalances are detected earlier, but resource consumption for monitoring increases
Solution Approach 1:
The patent implements dynamic monitoring intervals that adjust based on system conditions. The system can increase monitoring frequency when traffic load patterns indicate potential imbalances or when critical services are detected, and reduce frequency during stable periods. This dynamic approach maintains high reliability for failure prevention while optimizing resource consumption by avoiding unnecessarily frequent monitoring during low-risk periods.
Data Source
AI summary
Methods and systems for collecting, analyzing, and presenting traffic loads in each part of a storage area network are described. These methods and systems account for various resource types, logical access paths, and relationships among different storage environment components. Data traffic flow is managed in terms of resource planning and consumption. The aggregated information is stored, and may be used to estimate future data traffic loads or determine deviations between projected and actual traffic load status from which adjustments may be made to better predict and manage future data traffic load.