Predicting Upgrade Completion Times in Hyper-Converged Infrastructure
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Solution Overview
Problem
Hyper-converged infrastructure upgrades are lengthy and inconvenient due to the complexity of the architecture, making it difficult for implementers and users to manage upgrade times effectively.
Innovation Solution
A method and system that uses machine learning to predict upgrade completion times by processing static and dynamic indicators, employing a projected upgrade time service that includes a historical tuple pool, learning model optimizer, service kernel, and service API to provide accurate projected upgrade times to managers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hyper-converged infrastructure architecture is implemented, then infrastructure integration and management are improved, but upgrade time increases significantly
Solution Approach 1:
The system performs preliminary analysis of cluster indicators (CPU, memory, storage, network metrics) before executing upgrades to predict completion time in advance. This allows managers to plan upgrades during maintenance windows or low-utilization periods, preventing disruption to production workloads while maintaining the integrated HCI architecture benefits
2Measurement precision
If comprehensive cluster monitoring is implemented, then upgrade prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts and monitors only the most critical cluster indicators (CPU utilization, memory usage, storage capacity, network throughput) that have the greatest impact on upgrade completion time. By focusing on these key metrics rather than all possible system parameters, the solution achieves high prediction accuracy while avoiding excessive system complexity
Solution Approach 2:
The system continuously collects cluster indicator data during upgrade processes and uses this feedback to refine prediction models. Historical upgrade data and real-time cluster metrics are fed back into the prediction algorithm, improving accuracy over time without requiring proportional increases in system complexity
Data Source
AI summary
A method and system for predicting upgrade completion times in hyper-converged infrastructure (HCI) environments. Specifically, the method and system disclosed herein entail applying machine learning to forecast these upgrade completion times based on select static and dynamic indicators deduced to significantly impact the performance of upgrade processes across node clusters in HCI environments.