AI Media Access Manager for Storage Drive Scheduling
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Solution Overview
Problem
Conventional storage drive management techniques lead to inefficient and conflicted access to storage media, resulting in degraded performance due to internal operations interfering with data access requests, causing increased latency and reduced throughput.
Innovation Solution
An AI-enabled media access manager that uses artificial intelligence models to predict host system behavior and schedule host input/output operations, optimizing access to storage media by preempting or delaying internal operations based on predicted idle times and activity levels, thereby minimizing conflicts and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If internal drive operations are performed to maintain storage media health, then storage media reliability is improved, but storage drive performance deteriorates due to conflicting access with data requests
Solution Approach 1:
The AI engine predicts future host I/O operations in advance and uses these predictions to schedule internal drive operations during predicted idle periods. This preliminary action allows the system to prepare maintenance tasks before they are needed, executing them during periods when the host system is not accessing data, thereby preventing performance degradation while maintaining storage media health.
Solution Approach 2:
The system continuously monitors actual host I/O patterns and compares them with AI predictions. This feedback loop allows the AI engine to refine its prediction accuracy over time and adjust the scheduling of internal operations dynamically. The feedback mechanism ensures that internal operations are scheduled during actual idle periods, optimizing the balance between storage media maintenance and performance.
2Productivity
If data access requests are serviced immediately, then storage drive throughput is improved, but data request latency increases when internal operations conflict
Solution Approach 1:
The AI engine predicts upcoming idle periods in advance and schedules internal operations during these predicted idle times. By performing maintenance operations before they are needed (during idle periods), the system prevents conflicts with subsequent data access requests, thereby reducing data request latency while maintaining high throughput during active periods.
Solution Approach 2:
The system dynamically adjusts the timing and priority of internal operations based on real-time host I/O patterns and AI predictions. This dynamic scheduling allows the system to flexibly respond to changing workload conditions, ensuring that internal operations are executed at optimal moments that minimize impact on data access performance and reduce latency.
3Device complexity
If conventional scheduling is used for storage media access, then device complexity is reduced, but storage drive performance deteriorates due to uncoordinated access conflicts
Solution Approach 1:
The AI engine acts as an intermediary between the host I/O operations and the internal drive operations. It receives information about host I/O patterns, predicts future access needs, and uses these predictions to coordinate the scheduling of internal operations. This intermediary layer harmonizes the scheduling of conflicting operations without requiring complex changes to the host system or storage media, thereby improving performance while maintaining manageable device complexity.
Data Source
AI summary
The present disclosure describes apparatuses and methods for artificial intelligence-enabled management of storage media. In some aspects, a media access manager of a storage media system receives, from a host system, host input/output commands (I/Os) for access to storage media of the storage media system. The media access manager provides information describing the host I/Os to an artificial intelligence engine and receives, from the artificial intelligence engine, a prediction of host system behavior with respect to subsequent access of the storage media. The media access manager then schedules, based on the prediction of host system behavior, the host I/Os for access to the storage media of the storage system. By so doing, the host I/Os may be scheduled to optimize host system access of the storage media, such as to avoid conflict with internal I/Os of the storage system or preempt various thresholds based on upcoming idle time.