Device Cloud Orchestration for Distributed Microservice QoS
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Solution Overview
Problem
Existing wireless communication systems, particularly 5G NR, face challenges in optimizing distributed application performance due to complex service quality management in dynamic environments, leading to increased resource consumption and degraded performance.
Innovation Solution
A method for service quality aware device cloud orchestration that involves generating a 'Service Profile' defining compute and communication resource requirements for microservices, enabling optimal distribution and configuration of microservices across worker nodes and channels based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service quality management is implemented in dynamic wireless environments, then application performance and reliability are improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments service quality management into fine-grained microservice-level parameters (compute resources, communication resources, latency requirements) rather than managing entire applications as a monolith. This allows independent optimization of each microservice's quality parameters, reducing overall system complexity while maintaining high reliability.
Solution Approach 2:
The system dynamically adjusts service quality parameters in real-time based on current network conditions and microservice performance metrics. The orchestration platform continuously monitors and adapts resource allocation, communication channels, and quality of service settings to match changing environmental conditions, ensuring optimal performance without requiring complex static configurations.
2Productivity
If compute and communication resources are allocated to meet service quality requirements, then microservice performance is improved, but overall resource consumption increases
Solution Approach 1:
The patent changes resource allocation parameters dynamically based on actual service quality requirements and current workload demands. The system adjusts compute resource allocation, communication bandwidth, and latency tolerance parameters in real-time, allocating resources efficiently rather than provisionally, thereby reducing overall resource consumption while maintaining required performance levels.
Solution Approach 2:
The orchestration platform implements feedback mechanisms that continuously monitor microservice performance metrics and resource utilization. Based on this feedback, the system automatically adjusts resource allocation decisions, ensuring that compute and communication resources are assigned only where and when needed to meet service quality requirements, preventing waste and optimizing overall resource efficiency.
Data Source
AI summary
In an aspect of the disclosure, a method, a computer-readable medium, and a system are provided. The method is implemented by one or more computing devices. The one or more computing devices obtain a service profile for a distributed application. The service profile includes microservice parameters defining compute resource requirements for a plurality of microservices of the distributed application; and communication parameters defining communication resource requirements between the plurality of microservices. The one or more computing devices distribute the plurality of microservices across a plurality of worker nodes based on the service profile. The one or more computing devices configure communication channels between the distributed microservices based on the communication parameters.