Network Socket Sub-Sampling for Storage I/O Workload Capture
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Solution Overview
Problem
Current storage server technologies face challenges in efficiently collecting and analyzing workload data without disrupting the primary storage operations, requiring a mechanism that minimizes resource consumption and supports real-time analytics while maintaining high data integrity.
Innovation Solution
A method and system for selectively collecting and sub-sampling I/O request and response data from storage servers, allowing for real-time data capture and transmission over a network connection without interfering with standard I/O operations, using a data collection module that interfaces with a management console to analyze the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive workload data is collected from storage servers, then data integrity and analytics quality are improved, but storage server resource consumption and overhead increase
Solution Approach 1:
A network socket acts as an intermediary between the storage server and the data collection system. The storage server sends workload data through the network socket without requiring direct integration or additional processing resources on the storage server itself, thereby maintaining data integrity while minimizing server overhead
Solution Approach 2:
The data collection functionality is extracted from the storage server's core processing path. By using network sockets for data transmission, the workload monitoring is separated from the primary storage operations, allowing comprehensive data collection without impacting storage server performance
2Speed
If real-time workload data is captured continuously, then analytics responsiveness is improved, but resource overhead and system impact increase
Solution Approach 1:
The system uses periodic sub-sampling of workload data instead of continuous capture. Network sockets enable periodic data transmission at optimized intervals, providing responsive analytics while reducing the complexity and resource demands of continuous monitoring
3Productivity
If selective data collection is implemented, then resource efficiency is improved, but data completeness may be compromised
Solution Approach 1:
The system implements sub-sampling of workload data, collecting a representative partial set of I/O requests and responses. This partial action approach provides sufficient data for effective analytics and tuning while maintaining high collection efficiency, avoiding the need to capture every single data point
Data Source
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AI summary
Systems and. methods for: efficient input/output (I/O) workload, capture are provided. For example, in one aspect, a machine implemented method includes: opening a network socket for listening to a connection request from a computing device; accepting the connection request from the computing device over the network, socket; enabling selective data collection based on network connection with the computing device over the network socket, where the network connection based selective data collection includes obtaining information regarding a plurality of input/output (I/O) requests and responses and performance information of a storage server for processing the I/O requests sub-sampling the network connection based collected data; and sending at least a portion of the network connection based collected data over the network socket connection to : the computing device.