NLP Workflow Model Selection for Peak Query Load Handling
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Solution Overview
Problem
NLP-aaS solutions face resource inefficiencies due to varying client query loads, leading to idle capacity and increased costs from overprovisioning to meet service level agreements, especially during sharp peaks in query frequency.
Innovation Solution
A dynamic selection of machine learning models based on available service provider resources, optimizing the execution order and resource utilization across multiple stages of the NLP workflow to efficiently process queries and reduce redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing resources are overprovisioned to meet service level agreements during sharp peaks in query frequency, then service reliability is improved, but resource efficiency deteriorates due to idle capacity
Solution Approach 1:
The system dynamically adjusts the provisioning and allocation of computing resources based on real-time query load conditions. During peak loads, additional resources are activated to meet service level agreements, while during low-utilization periods, resources are de-provisioned or reallocated to eliminate idle capacity and improve efficiency.
Solution Approach 2:
The cloud-based NLP system is designed to serve multiple clients and workloads using a shared pool of computing resources. The same infrastructure can dynamically serve different NLP tasks and multiple customers, maximizing resource utilization across diverse workloads rather than dedicating fixed resources to single functions.
2Productivity
If more computing resources are allocated to handle sharp peaks in query frequency, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements monitoring and feedback mechanisms that track query load, resource utilization, and service level agreement compliance in real-time. Based on this feedback, the system automatically adjusts resource allocation and scaling decisions, enabling dynamic optimization without manual intervention and reducing the complexity of managing complex resource provisioning.
Data Source
AI summary
Examples described herein include a natural language processing (NLP) workflow for determining answers to queries. A query is received from a first client of a plurality of clients. A set of machine learning (ML) models are selected based on available service provider resources for processing the query. Each of the set of ML models corresponds to a respective stage of a NLP workflow. The query is input to a first model of the set of ML models. According to the NLP workflow, results from the first model are input to a second model of the set of ML models to determine a final result. A query answer based on the final result is transmitted to the first client.