Microservice Deployment Adjustment for Response Time Optimization
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Solution Overview
Problem
Current microservice management systems in centralized cloud computing environments face challenges in optimizing global service response time due to difficulties in adjusting microservice deployment, especially when considering load status and transmission delays between microservice entity devices.
Innovation Solution
A microservice management system that adaptively adjusts the deployment position of microservice entity devices based on load information and transmission delays, using a microservice deployment device to obtain service processing information, generate deployment update configuration, and adjust deployment positions to reduce service response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If microservice deployment is adjusted in centralized cloud computing environment, then deployment flexibility is improved, but global service response time optimization is insufficient
Solution Approach 1:
The patent implements dynamic deployment adjustment by continuously monitoring load information and transmission delays, and automatically repositioning microservice entities in real-time based on changing conditions. This dynamic mechanism enables the system to adapt deployment configurations on-the-fly, resolving the contradiction between deployment flexibility and response time optimization by making the deployment state adjustable and responsive rather than static.
Solution Approach 2:
The patent establishes a feedback loop that collects load information from microservice entities and transmission delay metrics, processes this data through the determination unit, and uses the results to guide deployment adjustments. This feedback mechanism ensures that deployment decisions are based on actual system performance data, enabling continuous optimization of global service response time while maintaining deployment flexibility.
2Productivity
If microservice entities are distributed across multiple computing resource pools, then system scalability is improved, but service response time increases due to transmission delays
Solution Approach 1:
The patent applies local quality by determining optimal deployment positions for each microservice entity based on its specific load information and the transmission delays to other entities in the service chain. Rather than using a uniform deployment strategy, the system tailors the deployment position of each entity to minimize its contribution to overall response time, allowing scalability while optimizing local interaction efficiency.
Solution Approach 2:
The patent changes the deployment position parameter of microservice entities based on dynamically determined optimal positions that consider load information and transmission delays. By adjusting this key parameter responsive to system conditions, the patent enables the system to maintain scalability across multiple resource pools while optimizing response time through parameter-based deployment control.
3Stability of the object's composition
If deployment position is fixed for microservice entities, then system stability is improved, but ability to optimize service response time under varying load conditions deteriorates
Solution Approach 1:
The patent transforms the static deployment position into a dynamic parameter that can be adjusted in response to changing load conditions. The determination unit continuously evaluates load information and transmission delays, and the deployment position is updated accordingly, enabling the system to maintain stability through controlled, data-driven adjustments rather than fixed configurations.
Solution Approach 2:
The patent implements self-service by enabling the microservice management system to automatically determine and adjust optimal deployment positions based on its own monitored performance data. The system uses its collected load information and transmission delay metrics to make autonomous deployment decisions, eliminating the need for external intervention while maintaining both stability and adaptability.
Data Source
AI summary
Embodiments of the present disclosure may provide a microservice management system, device, and apparatus. The system may include a microservice deployment device, a plurality of computing resource pools, and a target service chain. The target service chain may include at least one target microservice entity device, which may be from at lone of the plurality of computing resource pools. The microservice deployment device may be configured to obtain service processing information of the target service chain, generate a deployment update configuration information according to the service processing information of the target service chain, and adjust a deployment position of each of the at least one target microservice entity device on the target service chain according to the deployment update configuration information.


