Annotator Scheduling via Queuing Model in QA Pipelines
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Solution Overview
Problem
Existing question answering (QA) systems face inefficiencies in scheduling the execution of annotators, leading to suboptimal resource utilization and performance, as they lack precise timing and dependency management for pre-execution operations, which hampers concurrent execution and overall system performance.
Innovation Solution
A method utilizing an open queuing network model to approximate and optimize the response time of annotators in a QA system pipeline, where each annotator is represented as a node in a tandem queue, allowing for the calculation of effective response times and pre-execution start intervals to schedule operations optimally, thereby improving resource allocation and reducing bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If annotators execute operations sequentially without optimized scheduling, then the system is simpler to implement, but resource utilization is suboptimal and performance is hampered
Solution Approach 1:
The patent calculates pre-execution start intervals based on effective response times of annotators, allowing pre-execution operations to be scheduled in advance. This ensures that annotators have their required data ready before they need to execute, optimizing resource utilization by eliminating idle waiting time while maintaining a manageable scheduling approach through deterministic interval calculations.
2Loss of time
If pre-execution operations are scheduled later, then the scheduling mechanism is simpler, but processor cycles are wasted and response time increases
Solution Approach 1:
The patent computes pre-execution start intervals by subtracting effective response times from current execution times, enabling pre-execution operations to commence earlier. This reduces the loss of time by ensuring data is prepared in advance, while the timing management remains systematic through the use of response time-based interval calculations rather than complex adaptive scheduling.
3Productivity
If annotators run in parallel without dependency management, then concurrent execution is improved, but execution errors may occur due to missing dependencies
Solution Approach 1:
The patent schedules pre-execution operations to complete before annotators need their data, ensuring dependencies are satisfied in advance. This allows annotators to execute in parallel while maintaining reliability, as the systematic advance scheduling guarantees that required data will be available when needed, preventing execution errors.
Data Source
AI summary
Mechanisms are provided for scheduling execution of pre-execution operations of an annotator of a question and answer (QA) system pipeline. A model is used to represent a system of annotators of the QA system pipeline, where the model represents each annotator as a node having one or more performance parameters indicating a performance of an execution of an annotator corresponding to the node. For each annotator in a set of annotators of the system of annotators, an effective response time for the annotator is calculated based on the performance parameters. A pre-execution start interval for a first annotator based on an effective response time of a second annotator is calculated where execution of the first annotator is sequentially after execution of the second annotator. Execution of pre-execution operations associated with the first annotator is scheduled based on the calculated pre-execution start interval for the first annotator.