Dynamic Flow Control in Distributed Storage Systems
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Solution Overview
Problem
Distributed storage systems face challenges in efficiently managing I/O requests due to node overload and unbalanced workload distribution, leading to increased operational latency and potential system instability.
Innovation Solution
The system employs a method where I/O requests are decomposed into internal commands and executed across multiple nodes, with bandwidth limiters dynamically adjusting based on latency metrics to manage concurrent requests and reconfigure node workloads, ensuring balanced processing paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If I/O requests are decomposed and distributed across multiple nodes, then system throughput and processing speed are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments I/O requests into multiple internal commands that are distributed across different nodes in the storage system. Each node independently processes its assigned commands, enabling parallel execution and improving overall throughput while maintaining manageable complexity through clear division of labor
Solution Approach 2:
The patent implements universal node structures where each node can function as a receiver, processor, and forwarder of commands. This multi-functionality allows flexible workload distribution and simplifies system management by using standardized node interfaces throughout the distributed system
2Reliability
If bandwidth limiters are used to control concurrent requests, then system stability is improved, but processing throughput may be reduced
Solution Approach 1:
The patent implements dynamic bandwidth limiters that automatically adjust their constraints based on real-time system conditions such as node load, latency metrics, and queue depths. This dynamic adjustment allows the system to maintain stability under heavy load while maximizing throughput when resources are available, eliminating the need for fixed conservative limits
Solution Approach 2:
The patent employs feedback mechanisms where nodes report latency metrics and load conditions back to the bandwidth limiter, which then adjusts its control parameters accordingly. This closed-loop control ensures system stability while adapting to changing workloads, preventing both overload and unnecessary throttling
3Ease of operation
If latency metrics are collected from all nodes, then workload balancing is improved, but communication overhead and measurement complexity increase
Solution Approach 1:
The patent extracts only the essential latency metrics needed for workload balancing decisions from the complex node operation data. By focusing on key performance indicators such as command execution time and queue depth rather than collecting all possible node statistics, the system achieves effective workload balancing with minimal measurement overhead
Solution Approach 2:
The patent introduces intermediary aggregation logic that collects and processes latency metrics from multiple nodes, transforming raw measurements into balanced workload decisions. This intermediary layer simplifies the measurement complexity by handling metric aggregation and interpretation centrally or semi-centrally, reducing the burden on individual nodes
Data Source
AI summary
A method for processing I/O requests that are received at a distributed storage system including a plurality of receiver nodes, a plurality of first nodes, and a plurality of second nodes, the method comprising: receiving, at a receiver node, an I/O request and executing the I/O request by using at least one of the first nodes and at least one of the second nodes; receiving, by the receiver node, one or more latency metrics from each of the first nodes and second nodes that are used to execute the I/O request, and reconfiguring the storage system, by the receiver node, based on any of the received latency metrics.