Die-Aware Hierarchical Queues for Storage Latency and Throughput
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Solution Overview
Problem
Storage systems face challenges in optimizing latency and throughput under varying conditions, especially in complex environments with multiple types of storage memory and competing data operations, leading to inefficiencies in managing reads, writes, foreground and background operations, and data migration.
Innovation Solution
A die-aware scheduler with a hierarchical queue system is implemented, which aligns queues with individual storage memory dies and adjusts them based on real-time throughput monitoring to optimize latency and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a hierarchical queue system aligned with storage memory dies is implemented, then latency and throughput are optimized, but device complexity increases
Solution Approach 1:
The queue system is segmented into multiple hierarchical levels (foreground queue, background queue, and per-die queues) that are aligned with storage memory dies. This segmentation allows independent management and optimization of I/O operations for each die, improving throughput by enabling parallel processing while maintaining organized complexity through structured division.
Solution Approach 2:
The patent introduces a hierarchical dimension to queue management, organizing queues in multiple levels (foreground/background and per-die) rather than a single flat structure. This dimensional organization enables sophisticated scheduling and optimization across different operational contexts while maintaining manageable complexity through hierarchical abstraction.
2Productivity
If real-time throughput monitoring and dynamic adjustment is implemented, then latency and throughput are optimized, but device complexity increases
Solution Approach 1:
The system implements real-time throughput monitoring that continuously measures I/O performance metrics and feeds this information back to the scheduler. The scheduler dynamically adjusts queue assignments and scheduling parameters based on this feedback, enabling adaptive optimization of latency and throughput while managing complexity through automated closed-loop control.
Solution Approach 2:
The queue system transitions from static to dynamic configuration, where queue assignments, priorities, and scheduling parameters are continuously adjusted based on real-time monitoring of throughput and latency metrics. This dynamic adaptation enables the system to optimize performance under varying workload conditions while the automated nature of the adjustments prevents complexity from becoming unmanageable.
Data Source
AI summary
A die-aware scheduler that has a hierarchical queue is suitable for use in data storage systems. The hierarchical queue includes a priority queue, a die queue, a write queue and a power token queue, and may also include an admission queue. The die queue, the write queue and the power token queue have a width and lanes corresponding to dies in solid-state storage. The hierarchy of queues has dynamic adjustability of a ratio relating to handling queue items in the hierarchy of queues, to optimize latency and throughput.