SSD Queue Scheduling Across Storage Nodes and Channels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solid-state drives are designed to conform to hard disk drive standards, limiting their ability to leverage unique characteristics of flash and other solid-state memories, leading to scheduling conflicts and inefficiencies in storage systems.
Innovation Solution
A method of applying scheduling policies to non-volatile solid-state storage systems that utilize multiple operation queues and distributed control across storage nodes, allowing for global information-based decision-making and enhanced parallelism, with erasure coding and redundant metadata to ensure data availability and recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If solid-state drives conform to hard disk drive standards for compatibility, then compatibility is improved, but the ability to leverage unique characteristics of flash memory and avoid scheduling conflicts deteriorates
Solution Approach 1:
The patent segments the storage system into multiple independent storage nodes instead of treating it as a monolithic unit. Each node operates autonomously with its own scheduling policies, allowing the system to leverage flash memory characteristics while maintaining compatibility through standardized interfaces. This segmentation enables parallel operations across nodes, improving overall productivity without sacrificing compatibility.
2Productivity
If multiple operations are executed in parallel across storage channels, then productivity is improved, but scheduling conflicts worsen
Solution Approach 1:
The patent implements dynamic scheduling policies that adapt to real-time system state and workload characteristics. Each storage node independently evaluates operation priorities, channel availability, and buffer status to dynamically adjust scheduling decisions. This dynamic approach enables parallel operations across multiple channels while minimizing scheduling conflicts through coordinated but independent decision-making at each node.
Solution Approach 2:
The system incorporates feedback mechanisms where each storage node monitors its operational status, buffer occupancy, and channel utilization. This feedback information is used to adjust scheduling decisions in real-time, allowing the system to maintain high productivity through parallel operations while preventing scheduling conflicts by responding to actual system conditions rather than following rigid schedules.
3Reliability
If data is distributed across multiple storage nodes, then reliability is improved through fault tolerance, but system complexity worsens
Solution Approach 1:
The patent divides the storage system into multiple independent nodes, each capable of autonomous operation. Data is distributed across these segmented nodes with redundant copies stored on different nodes. This segmentation provides fault tolerance and data availability while managing complexity through standardized, repeatable node architectures that can be added or removed without redesigning the entire system.
Solution Approach 2:
The system creates redundant copies of data across multiple storage nodes. When data is written to one node, identical copies are simultaneously written to other nodes. This copying strategy ensures data availability and fault tolerance while simplifying the recovery process - if one node fails, the system can immediately serve requests from replica nodes without complex reconstruction algorithms.
4Productivity
If scheduling decisions are made based on global information, then productivity is improved, but communication overhead and system complexity worsen
Solution Approach 1:
The patent resolves the contradiction between global optimization and communication overhead by segmenting the decision-making process. Each storage node makes scheduling decisions independently based on locally available information about its own state and workload. This eliminates the need for continuous global communication and coordination, reducing complexity while maintaining productivity through distributed autonomous decision-making that optimizes local operations.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method of applying scheduling policies is provided. The method includes distributing user data throughout a plurality of storage nodes through erasure coding, wherein the plurality of storage nodes are within a single chassis coupling the storage nodes as a cluster. The method includes receiving operations relating to a non-volatile memory of one of the plurality of storage nodes into a plurality of operation queues. The method includes evaluating each of the operations in the plurality of operation queues as to benefit to the non-volatile solid-state storage. For each channel of a plurality of channels coupling the operation queues to the non-volatile memory, the method includes iterating a selection and an execution of a next operation from the plurality of operation queues, with each next operation having a greater benefit than at least a subset of operations remaining in the operation queues.