Dynamic Buffer and Thread Configuration for SSD Backup Systems
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Solution Overview
Problem
Conventional optimization solutions for hard disk drive-based backup systems are not applicable to solid state disk (SSD)-based backup systems, due to the unique read and write characteristics of SSDs, such as write amplification and garbage collection mechanisms, which affect performance.
Innovation Solution
A method that dynamically adjusts the number of threads and buffer size for SSD-based backup systems using a neural network to determine the optimal configuration based on the system state and reward score, improving performance and reducing management overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization solutions for hard disk drive-based backup systems are applied to SSD-based backup systems, then the system can be configured with fixed thread and buffer parameters, but the performance is degraded due to the unique read and write characteristics of SSDs such as write amplification and garbage collection mechanisms
Solution Approach 1:
The patent implements dynamic adjustment of thread count and buffer size parameters based on real-time monitoring of SSD state (wear level, temperature, I/O queue depth). The system transitions from static conventional configuration to dynamic adaptive configuration, allowing the backup system to optimize performance according to current SSD conditions while preventing write amplification and garbage collection interference.
Solution Approach 2:
The patent changes the values of key parameters (number of threads, buffer size) based on monitored SSD state metrics. When wear level is high, the system reduces thread count and adjusts buffer size to minimize write operations. When temperature increases, parameters are adjusted to prevent thermal throttling. This parameter adaptation resolves the contradiction between maintaining optimal performance and adapting to SSD-specific characteristics.
2Productivity
If the number of threads and buffer size are dynamically adjusted based on system state and reward score, then the backup performance is optimized, but the system complexity increases due to the need for state monitoring and neural network integration
Solution Approach 1:
The patent implements a self-service mechanism where the backup system automatically monitors its own state (I/O queue depth, buffer utilization, thread activity) and adjusts its configuration without external intervention. The neural network model receives state inputs and autonomously determines optimal parameter settings, eliminating the need for manual configuration and reducing operational complexity despite the sophisticated underlying algorithms.
Solution Approach 2:
The patent establishes a feedback loop where system performance metrics are continuously monitored and fed back to the neural network model. The reward score mechanism provides feedback on the effectiveness of current configurations, allowing the system to learn and adjust parameters iteratively. This feedback-driven approach simplifies management by replacing complex manual tuning with automated closed-loop control.
Data Source
AI summary
A method, electronic device, and computer readable medium for managing a backup system is disclosed. The method includes acquiring a state of the backup system. The backup system includes a backup server and at least one backup client terminal, the backup server using multiple threads to back up data from the at least one backup client terminal to a persistent storage device via a buffer. The method also includes determining a reward score corresponding to the state of the backup system and determining configuration information for the backup system based on the state and the reward score of the backup system. The configuration information indicating at least one of the number of the multiple threads and the size of the buffer. The number of writing threads and the size of the buffer for the persistent storage device may be dynamically adjusted based on the state of the backup system.