Edge Cloud QoS Alignment for Mobile Network Microservices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing edge computing systems fail to synchronize Quality of Service (QoS) control between mobile networks and edge clouds, leading to inconsistent and unpredictable end-to-end user experience for microservices-based applications.
Innovation Solution
A method to configure an edge cloud to meet end-to-end performance targets by aligning QoS parameters between mobile networks and edge clouds, involving the collection of network-side and cloud-side information, determination of microservice instances, and application of cloud-side QoS parameters and resource configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If QoS control is implemented independently in mobile networks and edge clouds, then each domain can optimize its own performance, but end-to-end QoS consistency deteriorates
Solution Approach 1:
The patent merges QoS control from independent mobile network and edge cloud domains into a unified end-to-end QoS framework. The network-side QoS parameters (5QI, ARP) are correlated with cloud-side QoS parameters through a mapping relationship, creating a combined control system that ensures consistent QoS across both domains rather than independent optimization.
Solution Approach 2:
The patent implements feedback mechanisms where the cloud-side performance information and network-side performance information are continuously monitored and used to adjust QoS parameters. The system uses performance targets and actual performance measurements to dynamically correlate and adjust QoS parameters across network and cloud domains, ensuring end-to-end QoS consistency.
2Reliability
If QoS parameters are dynamically adjusted to meet E2E performance targets, then user experience improves, but system complexity increases
Solution Approach 1:
The patent uses parameter changes by establishing mapping relationships between network-side QoS parameters (5QI, ARP) and cloud-side QoS parameters. Instead of complex dynamic adjustments, the system correlates parameters across domains using predefined relationships and performance targets, simplifying the control mechanism while maintaining E2E performance reliability.
Solution Approach 2:
The patent introduces an intermediary mapping relationship between network-side and cloud-side QoS parameters. This mapping acts as a mediator that translates and correlates QoS requirements across different domains, reducing the complexity of direct coordination between mobile network and edge cloud systems while ensuring consistent E2E performance.
3Productivity
If microservice instances are selected based on cloud-side performance targets, then application performance improves, but resource allocation complexity increases
Solution Approach 1:
The patent uses parameter changes by determining cloud-side performance targets based on network-side QoS parameters and E2E performance requirements. The system selects microservice instances by evaluating cloud-side performance information against these targets, using parameter-based filtering rather than complex multi-criteria optimization, thereby improving application performance while managing resource allocation complexity.
Data Source
AI summary
A method by one or more computing devices to configure an edge cloud to meet an end-to-end performance target for a microservices-based application that is implemented over the edge cloud and a mobile network is disclosed. The method includes determining a cloud-side performance target for the application based on the E2E performance target, network-side QoS control information, and network-side performance information, determining microservice instances of a microservice chain in the edge cloud that can be used to meet the cloud-side performance target for the application based on cloud-side service information, cloud-side resource usage information, and cloud-side performance information, determining cloud-side QoS parameters and a resource configuration for the microservice instances, and configuring the edge cloud to implement the microservice instances including applying the cloud-side QoS parameters and the resource configuration to the microservice instances.