Multi-Storage Data Management Using Persistent Temperature
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Solution Overview
Problem
Current database management systems lack efficient techniques for managing data storage across multiple storage environments, particularly in multi-storage systems that include both volatile and non-volatile storage, as they do not effectively utilize persistent temperatures to optimize data placement and retrieval.
Innovation Solution
The proposed solution involves managing data storage in multi-storage systems by using persistent temperature measurements from non-volatile storage to inform caching decisions in volatile storage, combining conventional caching techniques with temperature-based schemes to maintain 'hot' data and remove 'cold' data, thereby enhancing overall system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in volatile storage to improve access speed, then retrieval speed improves, but data loss risk increases when power is lost
Solution Approach 1:
The patent segments data into different temperature categories (hot, warm, cold) based on access patterns, and stores different segments in appropriate storage media. Hot data is kept in volatile storage for fast access, while cold data is stored in non-volatile storage for persistence, thus resolving the contradiction between speed and reliability through spatial segmentation of data.
Solution Approach 2:
The patent changes the parameter of storage medium selection based on data temperature characteristics. By dynamically adjusting which storage medium (volatile or non-volatile) is used for which data based on its access pattern parameter, the system achieves both fast retrieval for important data and reliable persistence for less critical data.
2Speed
If all data is kept in volatile storage to maximize access speed, then retrieval speed improves, but storage cost and energy consumption increase
Solution Approach 1:
The patent applies local quality by providing different storage qualities to different data based on their access patterns. Frequently accessed (hot) data receives high-quality volatile storage for fast access, while infrequently accessed (cold) data is placed in lower-quality non-volatile storage, optimizing the overall energy consumption while maintaining necessary access speeds.
Solution Approach 2:
Instead of keeping all data in volatile storage (excessive action), the patent keeps only the necessary portion (hot data) in volatile storage while moving cold data to non-volatile storage, thus reducing energy consumption while maintaining adequate access speed for active data.
3Ease of manufacture
If conventional caching techniques are used alone to manage volatile storage, then implementation simplicity is maintained, but optimization effectiveness decreases
Solution Approach 1:
The patent merges conventional caching techniques with temperature-based management to create a hybrid approach. This combination leverages the simplicity of traditional caching algorithms while adding the optimization power of temperature-aware data placement, thus improving productivity without significantly complicating the implementation.
Solution Approach 2:
The patent creates a universal storage management system that can handle both hot and cold data effectively. The temperature-based management mechanism serves multiple functions: it identifies data access patterns, determines optimal storage locations, and guides data movement decisions, making the system adaptable to various data types and access patterns while maintaining relatively simple implementation.
Data Source
AI summary
A combination of non-persistent-based and persistent-based schemes are used to effectively manage volatile storages which are conventionally managed solely by using non-persistent schemes (e.g., LRU schemes in cache memory). Generally, the persistent-based schemes can be based on persistent information associated with a non-volatile storage environment (e.g., persistent data temperatures associated with data stored in non-volatile storages). In this context, a persistent-based scheme can, for example, be effectively used in addition or combination with a conventional scheme provided for volatile memory. By way of example, a LRU scheme can be combined with a scheme based on persistent data temperatures in order to more effectively manage cache memory provided to enhance the performance of a system. As another example, a LRU, a LFU aging schemes can be combined with a scheme based on persistent data temperatures.