Prioritizing Diagnostic Data Bundles in Storage Systems
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Solution Overview
Problem
Large-scale data storage systems face significant delays in diagnosing and resolving performance issues due to the time-consuming process of transmitting extensive diagnostic data over networks, which hampers client satisfaction and efficiency.
Innovation Solution
The system prioritizes diagnostic data by segregating it into high and low priority bundles, with the most critical data sent first, allowing for early diagnosis and potential resolution before the complete dataset is transmitted, thereby reducing latency and improving client satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all diagnostic data is transmitted over the network, then complete diagnosis information is available, but transmission time becomes excessively long
Solution Approach 1:
The diagnostic data is divided into multiple priority levels (high, medium, low priority bundles), allowing critical data to be transmitted first while less critical data follows. This segmentation enables the diagnosis process to begin with essential information without waiting for the complete dataset, thereby reducing overall transmission time while maintaining diagnostic completeness.
Solution Approach 2:
The system performs preliminary classification of diagnostic data into priority bundles before transmission. By pre-identifying which data elements are critical for initial diagnosis versus those that can be transmitted later, the system enables early diagnostic intervention with high-priority data while the remaining data is still being prepared or transmitted.
2Measurement precision
If diagnostic data is transmitted in full, then accurate root cause identification is possible, but client waiting time increases significantly
Solution Approach 1:
The diagnostic data transmission is segmented into priority-based bundles, allowing the service provider to deliver high-priority diagnostic information first. This enables accurate root cause identification to begin with critical data while lower-priority data is transmitted in the background, reducing client waiting time without sacrificing diagnostic accuracy.
Solution Approach 2:
The system transmits a partial set of diagnostic data (high-priority bundle) that is sufficient for initial root cause identification and remediation. This partial action allows the service provider to address critical issues without requiring the complete dataset, thereby reducing client waiting time while maintaining adequate diagnostic accuracy for immediate problems.
3Reliability
If comprehensive diagnostic snapshots are collected, then complete system state is captured, but data transmission becomes a bottleneck
Solution Approach 1:
The comprehensive diagnostic data is segmented into priority-based bundles that can be transmitted in stages. High-priority bundles containing critical system state information are transmitted first, enabling rapid diagnosis of urgent issues. Lower-priority bundles are transmitted subsequently, maintaining complete system state capture while improving overall diagnosis throughput by enabling parallel processing of diagnostic tasks.
Data Source
AI summary
Implementations described and claimed herein provide systems and methods for prioritizing a support bundle. In one implementation, a fault indication specifying at least one fault of a storage device is generated. A request for support data for diagnosing a cause of the fault is received at the storage device. Low priority data for diagnosing the fault is identified from the support data. A prioritized support bundle is generated having a low priority bundle subset containing the low priority data and a high priority bundle subset containing remaining data in the support data for the storage device. The high priority bundle subset is sent to a diagnostic device over a network separately from the low priority bundle subset.