Dynamic Task Timeout Control for Clustered Storage Operations
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Solution Overview
Problem
In clustered computing systems, estimating a reasonable timeout period for task completion is challenging due to unpredictable resource possession times, leading to inefficiencies and potential task failures when environments are busy or underutilized.
Innovation Solution
Dynamic adjustment of timeout periods based on resource utilization rates and historical task data within the computing system, allowing for context-dependent optimization of task completion times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed timeout period is used for task completion, then the system structure is simple and easy to implement, but the system efficiency deteriorates due to unnecessary task expirations during high load conditions and prolonged operation times in less busy environments
Solution Approach 1:
The patent implements dynamic timeout adjustment by continuously monitoring resource utilization metrics and modifying timeout thresholds in real-time based on current system conditions. The timeout period transitions from a static fixed value to a dynamic value that adapts to changing load conditions, resolving the contradiction between simplicity and efficiency.
Solution Approach 2:
The system establishes a feedback loop where task completion progress and resource utilization are continuously monitored, and timeout thresholds are adjusted based on this feedback. The management system receives updates on task status and modifies timeout periods accordingly, enabling the system to respond to actual performance conditions rather than relying on predetermined fixed values.
2Reliability
If the timeout period is extended to prevent unnecessary expirations during high load, then task completion reliability improves, but operation time increases in less busy environments
Solution Approach 1:
The timeout period is made dynamic rather than fixed, allowing it to expand during high load conditions to prevent false expirations and maintain reliability, while contracting during low load conditions to minimize operation time. This dynamic adjustment directly resolves the contradiction between reliability and time loss.
Solution Approach 2:
The system changes the timeout parameter based on resource utilization metrics and task progress rates. By adjusting the timeout threshold parameter in response to changing system conditions, the system achieves both high reliability during busy periods and efficient operation during less busy periods.
3Productivity
If dynamic adjustment of timeout periods is implemented based on resource utilization, then system efficiency improves, but the complexity of timeout management increases
Solution Approach 1:
The system performs self-adjustment of timeout periods by automatically monitoring its own resource utilization and task progress, eliminating the need for external manual configuration or complex centralized control. The timeout management system serves itself by using internal metrics to drive timeout adjustments, reducing overall system complexity despite the dynamic behavior.
Data Source
AI summary
A storage system, comprising: a management system; and a storage processor that is coupled to the management system via a communications network, wherein the storage processor is configured to: receive an instruction to perform an operation; identify a plurality of tasks that are associated with the operation; store, in a database, a respective timeout threshold for each of the plurality of tasks; select one of the plurality of tasks; and update the respective timeout threshold of the selected task based on a utilization of a resource, the respective timeout threshold being updated while the operation is being performed by the storage processor; and wherein the management system is configured to: transmit the instruction to the storage processor; retrieve the respective timeout threshold of the selected task; detect whether the respective timeout threshold of the selected task is expired; when the respective timeout threshold is expired, abort the operation.