Latency-Managed Task Processing for Computing Systems
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Solution Overview
Problem
Computing systems face challenges in providing predictability and fairness in request completion times due to variability in resource availability and computational complexity, leading to inconsistent latency and potential overload for clients.
Innovation Solution
Implementing a method to manage backlogged tasks by determining latency and setting thresholds, limiting the number of outstanding tasks for requestor groups, and dynamically adjusting the number of pending tasks based on system capacity to ensure fair resource allocation and predictable performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the computing system accepts all client requests without limitation, then client service coverage is improved, but system overload and unpredictable latency occur
Solution Approach 1:
The system proactively limits the number of outstanding tasks for each requestor group before system overload occurs. By monitoring the count of pending tasks and comparing it against configured thresholds, the system prevents excessive task accumulation that would lead to unpredictable latency and resource exhaustion, thereby maintaining reliable service levels.
Solution Approach 2:
The system dynamically adjusts task acceptance based on real-time conditions. The latency-managed task processing mechanism monitors environmental factors such as resource availability and task queue depth, then adaptively controls whether to accept new requests. This dynamic adjustment allows the system to maintain predictability under varying load conditions while maximizing service coverage when capacity permits.
2Productivity
If the computing system prioritizes fast task processing, then productivity is improved, but fairness among clients deteriorates
Solution Approach 1:
The system segments clients into requestor groups and enforces per-group limits on outstanding tasks. This segmentation prevents any single client or group from monopolizing system resources, ensuring fair distribution of processing capacity across all clients while maintaining high overall productivity through parallel processing of multiple grouped requests.
Solution Approach 2:
The system changes the parameter of task acceptance by introducing latency-based thresholds. When the number of outstanding tasks exceeds the threshold, the system stops accepting new tasks from requestor groups, effectively controlling processing speed to maintain fairness. This parameter adjustment ensures no client can indefinitely delay others' requests while preserving system productivity within fair boundaries.
3Reliability
If the system monitors and limits outstanding tasks per requestor group, then fairness and predictability are improved, but system complexity increases
Solution Approach 1:
The system implements feedback control by continuously monitoring the number of outstanding tasks for each requestor group and comparing it against configured thresholds. When the threshold is exceeded, the system provides feedback by rejecting new tasks from that group until the queue length decreases. This simple feedback mechanism achieves predictable and fair task processing without requiring complex scheduling algorithms or resource management infrastructure.
Data Source
AI summary
A method and apparatus for managing backlogged tasks are disclosed. In the method and apparatus, upon receiving a task pertaining to a requestor group, a number of outstanding tasks associated with the requestor group is determined and the task is submitted for processing if the number of outstanding tasks is within an allowable range. If the number of outstanding tasks is outside of the allowable range, take one or more actions may be taken, which may include rejecting the request.