LLM Microservice Orchestration Across Edge and Cloud Tiers
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Solution Overview
Problem
Existing systems face challenges in efficiently orchestrating microservices across multi-tiered computing infrastructures, particularly in balancing cost and latency for distributed computing applications with dynamic workloads.
Innovation Solution
Leveraging Large Language Models (LLMs) to build cost and latency models, analyze microservice data, and learn trigger rules for optimal microservice placement, thereby dynamically orchestrating microservices across edge and cloud computing tiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If microservices are placed closer to users to reduce latency, then response time is improved, but operational cost increases
Solution Approach 1:
The patent implements dynamic microservice placement by continuously monitoring workload patterns and user distribution, then adjusting microservice locations in real-time. The system transitions from static placement to dynamic relocation based on changing conditions, allowing optimization of the latency-cost tradeoff at any given moment.
Solution Approach 2:
The system changes placement parameters of microservices based on analyzed workload characteristics. By modifying parameters such as location, replication factor, and resource allocation, the system optimizes the balance between response time and operational cost for different workload scenarios.
2Measurement precision
If custom models are rebuilt and re-deployed when changes are needed, then system accuracy is improved, but time and effort increase substantially
Solution Approach 1:
The patent pre-trains foundation models with comprehensive knowledge of microservice orchestration, cloud infrastructure, and workload patterns before deployment. This preliminary training enables the models to handle new scenarios and workload changes without requiring complete retraining, thus maintaining accuracy while reducing re-deployment time.
Solution Approach 2:
The system continuously monitors performance metrics and workload patterns, using this feedback to fine-tune and update models in an iterative manner. This feedback mechanism allows the system to adapt to changing conditions and improve accuracy over time without requiring substantial retraining from scratch.
3Adaptability or versatility
If complex customizations are implemented across multiple computing tiers, then system functionality is improved, but complexity and deployment effort increase
Solution Approach 1:
The patent employs foundation models that are universally applicable across multiple computing tiers (edge, fog, cloud) and various microservice workloads. These models serve multiple functions including workload analysis, placement optimization, and cost management, reducing the need for tier-specific customizations and simplifying deployment.
Solution Approach 2:
The foundation models act as intermediaries between different computing tiers and workload requirements. They translate complex multi-tier orchestration needs into standardized model responses, simplifying the deployment process while maintaining system functionality across heterogeneous environments.
Data Source
AI summary
Systems and methods for leveraging large language models for orchestrating microservices. A cost model and a latency model can be built with a large language model (LLM) based on an instruction prompt that analyzes microservice data of a distributed computing application. The microservice data can be analyzed to learn trigger rules that facilitate placement of microservices at optimal locations within the multi-tiered computing infrastructure based on cost and latency. The microservices can be placed at the optimal locations based on the trigger rules. The LLM can be tuned using feedback from the distributed computing application after placing the microservices at the optimal locations.