ML Latency Trend Throttling for Dedup File System Stability
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Solution Overview
Problem
Deduplicated file systems face performance degradation and potential shutdown due to sudden spikes in client requests and underlying storage system latency issues, leading to service disruptions.
Innovation Solution
Implementing a machine learning-based client throttling mechanism that tracks storage latency trends, throttling clients when latency increases, and withholding throttling when latency decreases, to prevent queue build-ups and maintain system stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If clients continue to issue requests to the file system without flow control, then client productivity is maintained, but the storage system experiences performance degradation and potential shutdown
Solution Approach 1:
The system implements a feedback mechanism by monitoring storage system performance metrics (latency, queue depth, throughput) and using this information to dynamically adjust client request rates. The file system analyzes performance data and sends feedback signals to clients to throttle or encourage request submission, creating a closed-loop control system that balances productivity and reliability
Solution Approach 2:
The flow control mechanism is dynamic rather than static. The system continuously adapts client throttling levels based on real-time storage system conditions, adjusting the degree of request rate limitation as performance metrics change. This allows the system to respond to varying workloads and storage capacity conditions
2Quantity of substance
If the file system performs deduplication operations to reduce redundant data, then storage efficiency is improved, but processing time increases during high request volumes
Solution Approach 1:
The system applies partial deduplication action by selectively performing deduplication operations based on current system conditions. During high load periods, the system may reduce the intensity or frequency of deduplication operations to maintain acceptable response times, while still achieving some level of redundancy reduction. This balances the benefit of storage efficiency against the cost of processing time
3Adaptability or versatility
If the storage system handles sudden spikes in demand from multiple tenants, then service coverage is improved, but infrastructure performance degrades
Solution Approach 1:
The system takes preliminary anti-action by proactively implementing flow control measures before the storage system becomes overwhelmed. When performance metrics indicate approaching capacity limits, the system preemptively throttles client requests to prevent saturation, rather than waiting for complete system failure. This anticipatory approach maintains service coverage while preventing throughput collapse
Data Source
AI summary
Detecting a trend in latency for storage underlying a deduplicated file system includes generating a set of data points by recording when input/output (IO) requests were issued to the storage and recording time required to receive success responses from the storage. Least squares regression is performed on the data points to find a best-fit line through the data points. A slope of the best-fit line is calculated. A determination is made as to whether the slope is positive, a positive slope thereby indicating a trend of increasing latency of the storage. When the slope is determined to be positive, clients accessing the deduplicated file system are throttled.