Automatic Service Profiling for Microservice Resource Allocation
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Solution Overview
Problem
Existing wireless communication systems, particularly 5G NR, face challenges in optimizing resource allocation and performance management for distributed microservices, leading to increased resource consumption and degraded performance due to complex infrastructure configurations and lack of transparency in underlying systems.
Innovation Solution
A mechanism for generating a service profile that defines parameters for managing resource allocation and quality of service (QoS) in distributed environments, including priority levels, bandwidth allocation, latency sensitivity, and adaptive QoS, to optimize performance and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex infrastructure configurations are used to support distributed microservices, then system functionality and connectivity are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system automatically generates service profiles by monitoring resource utilization and performance metrics, eliminating the need for manual configuration. The platform self-adjusts resource allocations and generates optimized profiles without requiring complex infrastructure setup or manual intervention, thereby reducing operational complexity while maintaining system functionality.
Solution Approach 2:
The system dynamically adjusts resource allocation parameters based on monitored performance metrics and generates optimized service profiles. By automatically changing parameters such as resource distribution and configuration settings, the system achieves optimal performance without requiring complex manual infrastructure configuration.
2Reliability
If manual service profile configuration is used, then control over resource allocation is improved, but productivity and ease of operation deteriorate
Solution Approach 1:
The platform automatically monitors resource utilization and generates service profiles without manual intervention. This automated self-service approach maintains reliable control over resource allocation while dramatically improving productivity by eliminating time-consuming manual configuration processes.
Solution Approach 2:
The system continuously monitors performance metrics and uses this feedback to automatically adjust resource allocations and generate optimized service profiles. This closed-loop feedback mechanism ensures reliable resource control while accelerating profile generation through automated data-driven decisions.
3Device complexity
If insufficient resources are allocated to microservices, then device complexity and cost are reduced, but reliability and performance deteriorate
Solution Approach 1:
The system monitors performance metrics and automatically adjusts resource allocations to ensure optimal service performance. This feedback-driven approach prevents under-provisioning by continuously adapting resource distribution based on actual performance data, maintaining high reliability without manual complexity.
Solution Approach 2:
The platform self-adjusts resource allocations based on monitored metrics, automatically ensuring sufficient resources are allocated to maintain service reliability. This automated resource management eliminates the trade-off between simplicity and performance by handling resource optimization autonomously.
Data Source
AI summary
In an aspect of the disclosure, a method, a computer-readable medium, and a system are provided. The method may be implemented by one or more computing devices. The one or more computing devices obtain an initial deployment configuration specifying connectivity between a plurality of microservices of a distributed application. The one or more computing devices deploy the plurality of microservices in a high-capacity computing environment. The one or more computing devices monitor resource utilization and performance metrics while executing the distributed application with sufficient resources in the high-capacity computing environment. The one or more computing devices generate multiple candidate service profiles by varying resource allocations for the plurality of microservices. The one or more computing devices collect performance measurements and quality of experience feedback for each candidate service profile. The one or more computing devices generate a final service profile based on the collected performance measurements and quality of experience feedback.