Dynamic Predictive Latency Attributes for Storage
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Solution Overview
Problem
Current storage devices operate within fixed deterministic windows (DTWIN) based on worst-case scenarios, leading to constrained quality of service and reduced DTWIN duration due to assumptions about end-of-life conditions and heavy workloads, which limits their operational efficiency.
Innovation Solution
Implementing a dynamic adjustment mechanism that tracks real-time host device workload characteristics and memory device conditions to adaptively determine and update DTWIN duration, allowing for a longer initial DTWIN that gradually decreases over time based on actual usage and device health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed DTWIN duration based on worst-case scenarios is used, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from a static, fixed DTWIN duration to a dynamic, adjustable DTWIN duration. The controller continuously monitors device health metrics and workload characteristics, then adapts the DTWIN duration in real-time. This allows the system to maintain reliability guarantees while optimizing productivity by extending DTWIN when conditions permit, rather than being constrained by worst-case assumptions throughout the device lifecycle.
Solution Approach 2:
The patent implements parameter changes by modifying the DTWIN duration parameter based on monitored conditions. Instead of using a single fixed parameter value, the system adjusts the DTWIN duration parameter dynamically according to device health status, workload intensity, and other operational factors. This parameter adaptation resolves the contradiction by allowing the system to operate with longer DTWIN periods when conditions are favorable while maintaining sufficient guarantees when conditions deteriorate.
2Reliability
If worst-case assumptions for host workload and memory device condition are made, then reliability is improved, but duration of action deteriorates
Solution Approach 1:
The patent applies feedback by implementing continuous monitoring of device health metrics and workload characteristics, then using this feedback information to adjust the DTWIN duration. The controller receives feedback about actual device performance and operational conditions, compares this against thresholds and patterns, and dynamically modifies the DTWIN duration accordingly. This feedback loop enables the system to maintain reliability while extending DTWIN duration beyond what worst-case assumptions would permit.
Solution Approach 2:
The patent implements preliminary action by proactively monitoring device health and workload conditions before they deteriorate to worst-case scenarios. The system anticipates potential issues by tracking trends in device metrics and adjusts DTWIN duration preemptively based on predicted future states. This allows the system to maintain longer DTWIN periods by preventing degradation to worst-case conditions rather than reacting after deterioration occurs.
3Ease of operation
If fixed DTWIN configuration is used, then ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The patent applies self-service by enabling the storage device to automatically adjust its own DTWIN duration based on monitored conditions without requiring external configuration or intervention. The controller autonomously monitors device health, evaluates workload characteristics, and modifies DTWIN parameters as needed. This self-service capability maintains ease of operation by eliminating complex configuration requirements while simultaneously achieving high adaptability to varying workload and device condition scenarios.
Data Source
AI summary
The present disclosure generally relate to dynamically changing predictive latency related attributes to increase the deterministic window (DTWIN) of operation. The host device workload characteristics as well as the memory device's current condition provide valuable information for the duration of the DTWIN. If the memory device is near the end of life, then the DTWIN duration will be smaller. Additionally, if the workload from the host device is heavy, then the DTWIN duration will also be smaller. Rather than utilizing a fixed DTWIN duration based upon worst case scenarios for host device workload and memory device condition, dynamically adjusting the DTWIN duration based upon the workload and condition will provide a DTWIN duration that can gradually decrease over time from a much longer DTWIN duration than is currently available.