Microservice Container Configuration via ML Resource Forecasting
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Solution Overview
Problem
Existing microservice configuration techniques in containerized systems face challenges in managing resource overutilization, leading to issues like out-of-memory errors, system latency, and failures.
Innovation Solution
The method involves collecting utilization information for microservices, generating resource usage forecasts using a machine learning model, combining these forecasts, and determining a new container configuration based on resource threshold values to mitigate overutilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If microservices are configured with fixed container resources, then system stability is maintained, but resource overutilization occurs leading to out-of-memory errors and system failures
Solution Approach 1:
The patent implements dynamic resource allocation for containerized microservices by continuously monitoring resource utilization metrics (CPU, memory, storage, network) and automatically adjusting container configurations based on current and forecasted demand. This replaces static resource allocation with adaptive mechanisms that respond to changing workloads, preventing both overutilization and underutilization while maintaining system reliability.
Solution Approach 2:
The system performs preliminary actions by forecasting future resource utilization using machine learning models before resource overutilization occurs. By analyzing historical utilization data and predicting future demands, the system proactively adjusts container configurations in advance, preventing out-of-memory errors and system failures before they happen rather than reacting after problems occur.
2Productivity
If container configurations are adjusted frequently to match resource demand, then resource utilization efficiency improves, but system instability and configuration churn increase
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor resource utilization metrics and feed this information back to the configuration management system. The machine learning models process this feedback data to identify genuine utilization patterns versus temporary fluctuations, enabling intelligent decision-making about when configuration changes are warranted. This feedback loop optimizes resource efficiency while filtering out noise that would cause unnecessary configuration churn.
Solution Approach 2:
The system carefully manages parameter changes by only adjusting container configurations when forecasted utilization patterns indicate sustained demand changes. Rather than responding to every fluctuation, the system applies parameter changes selectively based on confidence thresholds and trend analysis, thereby improving resource utilization efficiency while maintaining configuration stability and avoiding excessive churn.
3Reliability
If manual monitoring and adjustment of microservice resources is performed, then resource overutilization can be detected, but system complexity and operational overhead increase
Solution Approach 1:
The patent implements self-service automation where the system autonomously monitors resource utilization, forecasts future demands using machine learning models, and adjusts container configurations without human intervention. The automated system collects utilization data, processes it through forecasting algorithms, evaluates multiple configuration options, and deploys optimal configurations automatically. This eliminates manual monitoring and adjustment operations while maintaining reliable resource management, thereby reducing operational complexity and overhead.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for configuring microservices in containerized systems are provided herein. An example method includes collecting utilization information, over a first time period, for microservices that are implemented using a first container configuration, and generating, for each of the plurality of microservices, at least one corresponding forecast by processing the utilization information using a machine learning model, where the at least one forecast corresponding to a given one of the microservices predicts a utilization of the computing resources by the given microservice over a second time period. The method includes combining the generated forecasts to generate at least one combined forecast for the second time period, determining a second container configuration for the plurality of microservices by evaluating the at least one combined forecast against at least one resource threshold value, and initiating a deployment of the second container configuration of the plurality of microservices.