IO Request Scheduling for Foreground Response in LSM Storage
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Solution Overview
Problem
In data storage systems with a log-structured merge tree (LSM) structure, IO requests from foreground and background tasks compete for disk resources, leading to increased response times for foreground tasks and system stalls, affecting user experience.
Innovation Solution
Classify IO requests into traffic-unlimited first-type and traffic-limited second-type requests, dynamically adjusting the resource allocation based on load information to ensure foreground tasks are processed efficiently while optimizing background task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If background tasks are allowed to occupy IO resources freely, then background task execution is efficient, but foreground task response time increases and system stalls
Solution Approach 1:
The patent implements dynamic IO resource allocation by adjusting the traffic limiting proportion for background tasks based on real-time system load conditions. When foreground task response time exceeds a threshold, the system reduces the proportion of IO resources allocated to background tasks. This dynamic adjustment mechanism ensures that IO resources are flexibly reallocated between foreground and background tasks according to actual system state, resolving the contradiction between background task efficiency and foreground task responsiveness.
2Loss of time
If foreground tasks are prioritized for immediate processing, then user experience is improved, but background task execution is delayed
Solution Approach 1:
The patent implements periodic detection of system load conditions and periodic adjustment of traffic limiting proportions for background tasks. The system continuously monitors foreground task response times and periodically recalibrates resource allocation parameters. This periodic action ensures that background tasks are systematically managed with appropriate resource allocation over time, preventing both starvation and excessive resource consumption, thus balancing foreground responsiveness with background task completion.
3Adaptability or versatility
If IO resources are allocated dynamically based on load, then system adaptability is improved, but scheduling complexity increases
Solution Approach 1:
The patent achieves system adaptability through parameter changes in the traffic limiting proportion for background tasks. By adjusting this single key parameter based on monitored system load conditions, the system can adapt its resource allocation behavior without fundamentally changing the scheduling architecture. This parameter-based approach provides adaptability while controlling complexity, as it relies on modifying numerical parameters rather than implementing complex scheduling algorithms or multiple scheduling queues.
Data Source
AI summary
The specification provides an IO request scheduling method. The method includes: obtaining a target IO request to be scheduled, and determining whether the target IO request is a traffic-limited IO request: when the target IO request is a traffic-unlimited first-type IO request, determining a current moment as a scheduling moment of the target IO request: or when the target IO request is a traffic-limited second-type IO request, determining a scheduling moment of the target IO request based on a current resource traffic-limiting proportion; and adding the target IO request to an IO request set, where several IO requests in the IO request set are sequentially sent to a disk for processing in ascending order of scheduling moments.