Inline Deduplication Module Adapting to Network Congestion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Varying levels of data traffic on storage area networks (SANs) can impact the quality of service, leading to decreased performance for users due to network congestion, which affects the deduplication process and storage utilization.
Innovation Solution
An inline deduplication module adjusts its deduplication rate based on collected congestion information, using instructions to modify search times and transaction counts to optimize data transfer according to network conditions, thereby reducing congestion and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the deduplication module increases search time to look for data segment signatures, then deduplication completeness is improved, but network wait time increases and congestion worsens
Solution Approach 1:
The patent implements dynamic adjustment of deduplication parameters (search time, transaction counts) based on real-time network congestion conditions. The system transitions from static to dynamic operation by continuously monitoring network state and adapting deduplication intensity accordingly, allowing optimal balance between completeness and timing.
Solution Approach 2:
The system changes operational parameters (search time, transaction counts) based on network congestion levels. When congestion is detected, parameters are adjusted to reduce processing intensity; when network is clear, parameters are increased to improve deduplication completeness, thereby adapting to varying conditions.
2Productivity
If the deduplication module processes data at high rate, then storage utilization is improved, but network congestion increases and quality of service deteriorates
Solution Approach 1:
The system implements feedback control by monitoring network congestion indicators and using this information to adjust deduplication processing rates. The feedback loop continuously adapts processing intensity to network conditions, preventing overload while maintaining efficient storage utilization.
Solution Approach 2:
The system employs periodic monitoring of network congestion and adjusts deduplication rates in response to changing conditions. Rather than continuous maximum processing, the system rhythmically adapts to network state, processing intensively when conditions permit and reducing intensity when congestion occurs.
3Speed
If the organization stores data on on-premise devices, then data access speed is improved, but storage cost increases significantly
Solution Approach 1:
The system performs deduplication processing before data transfer to cloud storage, pre-processing data to eliminate redundancy. This preliminary action reduces the volume of data that needs to be stored remotely, thereby reducing cloud storage costs while maintaining access performance through local caching of deduplicated data.
Solution Approach 2:
The system creates deduplicated copies of data segments and stores references locally rather than storing complete redundant copies. This copying approach reduces the physical storage volume required both locally and in the cloud, lowering overall storage costs while maintaining data accessibility.
Data Source
AI summary
A computer-implemented method for managing an inline deduplication module is described. Congestion information is collected about a level of congestion on a network that communicates with an inline deduplication module. An instruction is sent to the inline deduplication module to adjust a deduplication rate based at least in part on the congestion information.