Predictive Cloud Patching Sequence Optimization
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Solution Overview
Problem
In cloud computing systems, patching thousands of pods is a cumbersome and time-consuming process, often resulting in operational issues due to the logistical challenges of applying patches across all pods simultaneously, with some pods needing critical patches that may not be included in initial batches, leading to potential operational problems and resource-intensive rollback processes.
Innovation Solution
A predictive patching system uses the Frequent Pattern Growth algorithm and matrix factorization to identify frequently occurring issues and their associations, generating a prioritized patching sequence that optimizes the application of patches based on the likelihood of issues across pods, ensuring that critical issues are addressed first and minimizing operational disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If patches are applied to all pods simultaneously in initial batches, then patching speed is improved, but critical issues may be missed on pods not included in initial batches leading to operational problems
Solution Approach 1:
The system performs preliminary analysis of issue patterns and pod characteristics before patching to predict which pods are most likely to experience critical issues. This predictive modeling allows the system to prioritize patching sequences in advance, ensuring that pods at highest risk are patched first while maintaining overall patching speed.
Solution Approach 2:
The system dynamically changes patching parameters (such as batch sizes, timing intervals, and priority assignments) based on predicted issue likelihoods for different pods. This allows optimization of both patching speed and reliability by adapting the patching process to the specific risk profile of each pod rather than applying a uniform approach.
2Reliability
If patches are applied sequentially to individual pods, then operational reliability is improved by addressing critical issues, but patching time and resource consumption increase
Solution Approach 1:
The system segments the pod population into priority groups based on predicted issue likelihood, rather than treating all pods uniformly. This segmentation allows parallel patching within each priority group while ensuring that high-risk pods are addressed first, thereby reducing total patching time while maintaining operational reliability.
Solution Approach 2:
The patching sequence is dynamically adjusted based on real-time predictions of issue likelihood for each pod. The system continuously updates priority assignments and reorders patching schedules as new information becomes available, optimizing the balance between reliability and time consumption throughout the patching process.
3Measurement precision
If comprehensive patching analysis is performed for all pods, then patching accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies different levels of analysis depth to different pods based on their individual risk profiles. Pods with higher predicted issue likelihood receive more comprehensive analysis, while low-risk pods receive streamlined assessment. This local differentiation maintains high patching accuracy for critical cases while reducing overall computational resource consumption.
Solution Approach 2:
The system extracts and focuses computational resources on the most critical prediction tasks - identifying which pods are most likely to experience issues. Rather than performing exhaustive analysis on all pods equally, the system concentrates analytical effort on the subset of pods that will benefit most from detailed prediction, thereby improving accuracy where it matters most while conserving resources.
Data Source
AI summary
Embodiments determine a patching sequence for applying software patches to correct a plurality of issues on a cloud system that comprises a plurality of pods. Embodiments receive a list of issues that occurred on each of the plurality of pods during a first time duration and, using a Frequent Pattern Growth algorithm, determine issues that frequently occur together and issues that likely follow other issues. Embodiments generate a matrix showing a number of each of the plurality of issues that occurred on each of the pods based on the list of issues during the first time duration, and update the matrix using matrix factorization to provide a relative ranking of each of the issues occurring on each pod. Embodiments then generate the patching sequence comprising a time ordered listing of combinations of pods and issues.