Request Throttling Manager for Distributed Storage QoS
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Solution Overview
Problem
Large-scale distributed data storage systems face challenges in providing guaranteed Quality of Service (QoS) due to varying data rates and priorities, leading to inefficient resource utilization and potential system failures when additional resources are increased without knowledge of existing utilization.
Innovation Solution
Implementing a request throttling manager with token buckets that queue and process file data requests based on available tokens, allowing for independent operation of request queues and token buckets with varying capacities, and adjusting token bucket capacities based on system throughput to manage resource allocation effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional resources are increased to provide QoS for incoming requests, then service quality is improved, but system cost and failure opportunities increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the current utilization of existing resources before making resource allocation decisions. The system determines resource utilization metrics (such as CPU usage, memory consumption, I/O operations) and uses this pre-acquired information to make informed decisions about whether to allocate additional resources or throttle incoming requests, thereby avoiding unnecessary resource increases that would raise costs and failure risks
Solution Approach 2:
The patent implements feedback by continuously monitoring resource utilization metrics and using this information to dynamically adjust request throttling decisions. The system establishes a feedback loop where resource utilization data is fed back to the throttling mechanism, enabling adaptive control that maintains QoS guarantees while optimizing resource usage and preventing over-provisioning
2Productivity
If additional resources are increased to handle diverse client requests, then service capacity is improved, but resource underutilization occurs
Solution Approach 1:
The patent applies dynamics by making the request throttling mechanism adaptive and flexible rather than static. The system dynamically adjusts throttling parameters based on real-time resource utilization conditions, allowing it to scale service capacity up or down as needed. This dynamic approach enables the system to handle diverse client requests effectively while avoiding resource underutilization by only allocating capacity when actually needed
Solution Approach 2:
The patent implements parameter changes by modifying throttling parameters (such as request rate limits, queue depths, or priority thresholds) based on measured resource utilization. The system changes these operational parameters dynamically to match actual system conditions, thereby optimizing service capacity while preventing resource underutilization that would occur with fixed, over-provisioned parameters
3Ease of operation
If resource allocation is increased without knowledge of existing utilization, then service availability is improved, but system efficiency deteriorates
Solution Approach 1:
The patent applies self-service by enabling the throttling mechanism to automatically determine resource utilization and make allocation decisions without external intervention. The system autonomously monitors its own resource usage, calculates utilization metrics, and adjusts request throttling accordingly, eliminating the need for manual resource provisioning while maintaining high service availability and efficiency
Data Source
AI summary
The disclosed technology includes an example system that has a request throttling manager that is configured to receive a first file data request, queue the first file data request in a first request queue, and process the first file data request based on the first token bucket. The first token bucket includes a sufficient first quantity of first tokens to process the first file data request. The system further includes a storage manager configured to access one or more storage nodes of a plurality of storage nodes of a distributed storage system in response to the first file data request.