Pod Update Prioritization Using Topology-Aware Risk Ratings
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Solution Overview
Problem
Existing methods for updating software in a distributed computing environment do not consider the structure of the cluster, leading to inefficient prioritization of vulnerability updates based on vulnerability scores alone, neglecting factors like network topology and criticality.
Innovation Solution
A computer-implemented method that calculates a risk rating for each pod in a distributed computing environment by considering vulnerability scores, network topology, and criticality, prioritizing updates based on these ratings to ensure high-priority pods are updated first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software is updated based solely on vulnerability scores, then the updating process is simple and fast, but the security effectiveness is reduced because cluster structure and criticality are not considered
Solution Approach 1:
The patent transforms the single parameter approach (vulnerability score only) into a multi-parameter risk rating system that incorporates vulnerability score, network topology (inbound hops), and criticality score. This parameter expansion resolves the contradiction by enabling more accurate prioritization that maintains security effectiveness while still being computationally efficient.
Solution Approach 2:
The patent introduces a risk rating as an intermediary metric that mediates between the simple vulnerability score and the complex security priorities. This intermediary calculation layer allows the system to balance simplicity and effectiveness by translating multiple factors into a single prioritization metric.
2Reliability
If the cluster structure and criticality are considered in update prioritization, then security effectiveness is improved, but the complexity of the updating process increases
Solution Approach 1:
The patent segments the risk assessment into distinct components: vulnerability score assessment, network topology analysis (inbound hops calculation), and criticality score assessment. Each component is calculated separately and then integrated into the final risk rating, making the complex process more manageable and systematic.
Solution Approach 2:
The patent manages complexity by formalizing the multi-parameter risk rating calculation with clear definitions for each parameter (vulnerability score, inbound hops, criticality score) and their integration method, transforming conceptual complexity into structured computational steps.
3Ease of operation
If all pods are updated simultaneously, then the updating process is simple to manage, but security risks persist because high-priority pods are not addressed first
Solution Approach 1:
The patent applies preliminary action by calculating risk ratings for all pods before the update process begins. This pre-assessment establishes a prioritization sequence that guides the subsequent update execution, ensuring high-risk pods are updated first while maintaining systematic management.
Solution Approach 2:
The patent introduces dynamic prioritization where the update sequence is determined by calculated risk ratings rather than static simultaneous updating. The system adapts the update order based on the specific risk profile of each pod, balancing management simplicity with targeted security risk mitigation.
Data Source
AI summary
A prioritized method is presented for updating software in a distributed computing environment. The objective of the disclosure is to find a computer-implemented method for updating software on pods in the distributed computing environment. This is solved by calculating a risk rating for each pod in the manifest, taking into account a vulnerability score of the pod, the minimum number of inbound hops for reaching the pod, and the communication relationship between the given pod and other pods. Pods in the manifest are updated in accordance with the calculated risk ratings.