Microservices Resource Orchestration in 5G Edge Networks
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Solution Overview
Problem
Deploying and optimizing microservices-based 5G applications in complex, dynamic, multi-tiered compute and network environments is challenging due to high variability in resource availability and real-time conditions, particularly in IoT and edge computing scenarios where coupling relationships between compute and network resources impact application performance.
Innovation Solution
The method involves jointly managing compute and network requirements by modeling and optimizing resource usage across multiple layers, employing coupling functions to determine optimal deployment and resource allocation decisions for microservices, leveraging historical data to model resource coupling relationships and solve multi-objective optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static resource allocation methods are used, then device complexity is reduced, but resource usage efficiency deteriorates significantly
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring real-time conditions (network traffic, compute load, coupling relationships) and adjusting resource allocation decisions accordingly. The system transitions from static pre-configured allocation to dynamic adaptive allocation, where resource allocation changes based on current system state and predicted future conditions.
Solution Approach 2:
The system performs preliminary actions by using historical data and machine learning models to predict future resource requirements and coupling relationships. Resource allocation decisions are prepared in advance based on predicted conditions, allowing the system to proactively optimize resource usage before actual demand patterns materialize.
2Loss of energy
If dynamic resource allocation is implemented, then resource usage efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary orchestration layer that sits between the microservices and the underlying compute/network resources. This intermediary handles the complexity of dynamic resource allocation, coupling relationship modeling, and real-time optimization, shielding individual microservices from complexity while enabling efficient resource usage through centralized intelligent control.
Solution Approach 2:
The orchestration system performs multiple functions simultaneously: it monitors real-time conditions, predicts future states using machine learning, models coupling relationships between resources, makes allocation decisions, and adjusts resources dynamically. This multi-functional approach consolidates complexity into a single universal system rather than requiring separate mechanisms for each function.
3Measurement precision
If coupling relationships between compute and network resources are modeled, then resource allocation accuracy is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual resource usage patterns, network traffic, and compute load, then using this feedback to refine coupling relationship models. Machine learning algorithms learn from observed correlations between compute and network resource usage, progressively improving measurement accuracy of coupling relationships through iterative feedback loops.
Data Source
AI summary
A method for performing resource orchestration for microservices-based 5G applications in a dynamic, heterogenous, multi-tiered compute and network environment is presented. The method includes managing compute requirements and network requirements of a microservices-based application jointly by positioning computing nodes distributed across multiple layers, across edges and at a central cloud, identifying and modeling coupling relationships between compute and network resources for a plurality of microservices, when only application-level requirements are provided, to build coupling functions, solving a multi-objective optimization problem to identify how each of the plurality of microservices are deployed in the dynamic, heterogenous, multi-tiered compute and network environment by employing the coupling functions to jointly optimize resource usage of the compute and network resources across different compute and network slices, and deriving optimal joint network and compute resource allocation and function placement decisions.