Adaptive Storage Client Compression for Cloud Latency Bottlenecks
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Solution Overview
Problem
Existing storage clients often perform poorly in cloud environments due to high latency and potential unavailability caused by network issues, limiting their ability to efficiently access and manage data across multiple back-end storage systems.
Innovation Solution
The implementation of a storage client that provides data compression and adaptive compression algorithms, along with caching and encryption capabilities, to optimize data handling and reduce latency, allowing access to multiple back-end storage systems and ensuring higher availability, especially in cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed using traditional storage clients, then data size is reduced, but latency increases due to remote cloud server communication
Solution Approach 1:
The patent segments the storage system into local cache components and remote storage components. The storage client divides data operations into local cache operations (fast) and remote storage operations (slower), allowing frequent accesses to occur locally without incurring cloud latency, while still achieving data size reduction through compression of cached data.
Solution Approach 2:
The patent introduces a local cache as an intermediary layer between the application and remote cloud storage. This intermediary stores compressed data locally, allowing the application to access compressed data quickly without direct cloud communication, thus reducing latency while maintaining data size reduction benefits.
2Quantity of substance
If compression frequency is increased to reduce data size, then storage efficiency improves, but processing overhead increases
Solution Approach 1:
The patent applies compression selectively rather than universally. It compresses data in the local cache where it provides maximum benefit, while using already-compressed or efficiently-stored data from remote storage. This partial application of compression reduces processing overhead while still achieving significant data size reduction for frequently accessed data.
Solution Approach 2:
The patent dynamically adjusts compression parameters and strategies based on data characteristics and access patterns. By changing compression frequency and intensity as needed rather than applying fixed compression, the system optimizes the balance between data size reduction and processing overhead.
3Speed
If local caching is implemented to reduce latency, then access speed improves, but memory usage increases
Solution Approach 1:
The patent applies different quality levels of data storage locally versus remotely. Frequently accessed data is cached locally in compressed form, while less frequently accessed data remains in remote storage. This local quality differentiation allows the system to optimize memory usage by storing only essential data locally while maintaining fast access speeds for critical data.
Solution Approach 2:
The patent implements dynamic cache management where the local cache size and contents adapt based on access patterns and available memory. The system dynamically adjusts what data to cache and for how long, allowing access speed to be maintained for important data while memory usage remains flexible and optimized.
4Quantity of substance
If adaptive compression is used to optimize data size, then storage efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements self-service adaptive compression where the storage client automatically monitors its own cache performance, data access patterns, and compression effectiveness. The system self-adjusts compression strategies without external intervention, managing its own complexity internally while presenting a simple interface to users and applications.
Data Source
AI summary
A method, system, and computer program product for data compression in storage clients. In some embodiments, a storage client for accessing a storage service from a computer program is provided. A compression method is provided in the storage client to reduce a size of data objects. A frequency of compressing data from the computer program or modifying a compression algorithm based on assessing costs and benefits of compressing the data is varied.