Predictive Data Prefetching to Reduce Cold-Tier Retrieval Latency
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Solution Overview
Problem
Data retrieval from lower performance storage tiers, such as cold storage, results in increased latency and costs, while up-tiering data on demand can cause system load and resource consumption spikes.
Innovation Solution
Implement a data platform that uses predictive analysis and machine learning to prefetch data from secondary storage tiers to primary tiers based on predicted access times, reducing latency and managing system load by optimizing data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is stored on lower performance storage tiers to reduce costs, then storage efficiency and cost are improved, but data retrieval latency increases
Solution Approach 1:
The system performs preliminary actions by prefetching data from secondary storage to primary storage before the data is actually requested by applications. The predictive analytics engine identifies data that will be needed soon and initiates transfer operations in advance, so that when applications request the data, it is already available on high-performance storage tiers, eliminating latency without requiring permanent residence of all data on expensive storage.
Solution Approach 2:
The system implements self-service through automated predictive analytics and dynamic data tiering. The predictive analytics engine continuously monitors access patterns and automatically determines which data should be prefetched, while the data tiering system autonomously manages data movement between storage tiers based on predictions, reducing manual intervention and optimizing storage resource allocation dynamically.
2Reliability
If data is up-tiered on demand from secondary storage, then data availability is improved, but system load and resource consumption increase
Solution Approach 1:
The system performs data tiering operations in advance based on predictive analytics, spreading transfer operations over time before peak demand periods. This prevents concentrated system load during actual data access operations, as the heavy lifting of data transfer is completed proactively when system resources are more available, ensuring data availability without overwhelming system capacity.
Solution Approach 2:
The system dynamically adjusts data transfer operations based on real-time system conditions and predictive insights. The data tiering system monitors system load, available bandwidth, and storage capacity, dynamically modifying transfer rates and scheduling to optimize resource utilization. This dynamic adaptation ensures data availability while preventing system overload by scaling operations to match available resources.
3Speed
If more data is kept on primary storage tier, then data access speed is improved, but storage cost and capacity constraints increase
Solution Approach 1:
The system maintains a smaller footprint of frequently accessed data on primary storage by proactively prefetching additional data before access requests occur. This preliminary action allows the system to serve more data requests from primary storage without permanently storing more data there, as the prefetch buffer dynamically expands capacity for hot data temporarily, improving access speed while respecting capacity constraints.
Solution Approach 2:
The system changes the temporal parameters of data residence on storage tiers through predictive prefetching. Data that would normally reside on secondary storage is temporarily moved to primary storage for a predicted short duration based on access patterns. This parameter change in data lifecycle management allows the system to optimize for speed during predicted access windows while maintaining overall capacity efficiency through automated re-tiering after use.
Data Source
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AI summary
Techniques are disclosed for prefetching data using predictive analysis. An example method comprises storing, by a data platform implemented by a computing system, objects of a file system, wherein a first subset of the objects is stored to a first storage tier and a second subset of the objects is stored to a second storage tier, classifying objects into one or more classifications, storing a data access record for the objects, applying a machine learning model to generate a prediction of future data access to one or more objects of the second subset based on the one or more classifications and the data access record, wherein the prediction includes a predicted time for the future data access, and retrieving, based on the prediction, the one or more objects of the second subset from the second storage tier prior to the predicted time.