Server-Side Deduplication Cache Synchronization
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Solution Overview
Problem
Current data deduplication systems face inefficiencies in managing and synchronizing client-side and server-side caches, leading to suboptimal storage utilization and increased transfer times between clients and servers.
Innovation Solution
A method and system where a server maintains a catalog of commonly used deduplicated data chunks and provides selected chunks to clients to prepopulate, refresh, or update their caches, based on reference counts, dates, and data types, optimizing cache hit ratios and reducing unnecessary data transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If client-side deduplication cache is used to reduce storage space, then storage efficiency is improved, but cache synchronization complexity increases
Solution Approach 1:
The server proactively identifies and sends deduplicated chunks to clients before they are requested, pre-populating the client-side cache. This preliminary action eliminates the need for complex real-time synchronization protocols, as the cache is already populated with likely-needed data.
Solution Approach 2:
The system implements a feedback mechanism where the server monitors client cache hit ratios and adjusts chunk transmission accordingly. When the server detects that a client's cache is becoming ineffective (low hit ratio), it sends additional relevant chunks to optimize performance, creating a self-regulating synchronization system.
2Measurement precision
If server maintains comprehensive catalog of all chunks, then deduplication accuracy is improved, but data transfer time increases
Solution Approach 1:
Instead of transferring the entire catalog or all possible chunks to clients, the server selectively transmits only the subset of chunks that are most relevant to each specific client based on their data patterns and cache performance. This partial action approach maintains deduplication accuracy while minimizing transfer time.
Solution Approach 2:
The system tailors the catalog content to each client's specific needs by analyzing their data characteristics and cache performance locally. Each client receives a customized subset of the server's catalog that is optimized for their particular workload, rather than a universal comprehensive catalog.
3Productivity
If client cache is frequently updated to improve hit ratio, then cache effectiveness is improved, but network bandwidth consumption increases
Solution Approach 1:
The client monitors its own cache performance and autonomously requests additional chunks from the server when its hit ratio drops below optimal levels. This self-service approach allows the system to optimize cache effectiveness on-demand without requiring continuous server-initiated updates, thereby reducing unnecessary bandwidth consumption.
Solution Approach 2:
Instead of continuous cache updates, the system employs periodic monitoring of cache hit ratios and triggers chunk transmissions only when performance degradation is detected. This periodic action reduces network bandwidth consumption compared to continuous synchronization while maintaining adequate cache effectiveness.
Data Source
AI summary
A server computational device maintains commonly occurring duplicate chunks of deduplicated data that have already been stored in a server side repository via one or more client computational devices. The server computational device provides a client computational device with selected elements of the commonly occurring duplicate chunks of deduplicated data, in response to receiving a request by the server computational device from the client computational device to prepopulate, refresh or update a client side deduplication cache maintained in the client computational device.