Storage Device Power Mode Prediction for Background Operation Scheduling
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Solution Overview
Problem
Data processing systems experience performance degradation when housekeeping operations such as data migration and wear leveling are not performed in a timely manner due to high-priority processes or external requests, leading to inefficient use of resources.
Innovation Solution
A data processing system that predicts the time required for background operations like garbage collection and wear leveling, switches to a corresponding power mode during idle states, and suspends command processing requests during this time to ensure these operations are completed without performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If housekeeping operations are performed in a timely manner, then reliability of data storage is improved, but productivity of command processing deteriorates due to system idle time
Solution Approach 1:
The system performs housekeeping operations during idle time periods that are predicted and prepared in advance. The controller determines predicted idle time before it occurs and schedules background operations to execute during this time, ensuring both reliable data storage maintenance and continuous command processing without performance degradation.
2Productivity
If the system remains in active state to process commands, then productivity is improved, but energy consumption increases
Solution Approach 1:
The storage device dynamically transitions between different power modes (active, idle, sleep) based on predicted workload patterns and the timing of housekeeping operations. This dynamic power management allows the system to reduce energy consumption during periods when command processing is naturally low while maintaining productivity by scheduling maintenance tasks during these low-utilization periods.
3Reliability
If the system switches power modes frequently to perform background operations, then reliability of background operation completion is improved, but device complexity increases
Solution Approach 1:
The controller uses feedback from predicted idle time information to make intelligent decisions about power mode transitions and background operation scheduling. By receiving predicted idle time from the controller and using this information to determine when to switch power modes, the system avoids arbitrary or frequent transitions, reducing complexity while ensuring background operations complete reliably during appropriate time windows.
Data Source
AI summary
A data processing system may include a storage device configured to: transmit, to an exterior, prediction information, for each power mode, that indicates a predicted time for performing a background operation for managing a memory device; and perform the background operation in an idle state of the storage device by switching to a corresponding power mode in response to a power mode control signal that is received in the idle state; and a control device configured to: determine a power mode of the storage device and an idle time for the idle state during which the background operation is performed based on the prediction information; transmit the power mode control signal to the storage device; and suspend, during the idle time, execution of a command processing request transmitted to the storage device.