Network Troubleshooting Digital Assistant for SDN VNFs
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Solution Overview
Problem
The complexity and dynamic nature of software-defined networks (SDNs) with virtualized network functions (VNFs) make manual troubleshooting challenging due to intricate interactions, increased error rates, and the difficulty in identifying relevant data for troubleshooting, especially in large-scale cloud-based systems.
Innovation Solution
A digital assistant system that uses a knowledge base to gather and analyze contextual and problem information from various network sources, providing recommendations and updating its knowledge base based on user interactions to improve troubleshooting efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting is used in complex virtualized networks, then human expertise can be applied, but the process becomes extremely challenging, resource intensive and potentially inaccurate
Solution Approach 1:
The patent introduces a digital assistant as an intermediary between network operators and complex virtualized network systems. This assistant automatically collects data from multiple sources (VNFs, VMs, physical infrastructure), analyzes correlations, and presents structured troubleshooting recommendations, thereby mediating the complexity between human operators and the intricate virtualized environment.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with automated computational systems. The digital assistant uses algorithms to automatically gather data, identify correlations, and generate troubleshooting steps, substituting human manual analysis with automated information processing and pattern recognition capabilities.
2Adaptability or versatility
If virtualized services are made dynamic and scalable, then deployment flexibility improves, but manual troubleshooting becomes extremely challenging and resource intensive
Solution Approach 1:
The patent implements self-service capabilities where the digital assistant autonomously performs data collection, analysis, and recommendation generation without requiring manual intervention. The system automatically adapts to dynamic virtualized environments, collecting data from changing VNF configurations and presenting relevant troubleshooting information, thereby maintaining ease of operation despite deployment flexibility.
Solution Approach 2:
The patent performs preliminary actions by proactively collecting and organizing data from multiple network sources before troubleshooting is needed. The digital assistant continuously monitors and stores information about VNFs, VMs, and physical infrastructure, so when a problem occurs, the analysis is already partially complete, reducing the complexity of real-time troubleshooting.
3Quantity of substance
If the quantity of data from diverse sources is increased, then more information is available for troubleshooting, but it becomes challenging to determine which information is relevant
Solution Approach 1:
The patent applies local quality by providing customized, context-specific information to different users based on their roles and the specific problem being troubleshooted. The digital assistant filters and presents only the relevant data subsets for each situation, rather than overwhelming users with all available data, thereby making information relevance detection easier.
Solution Approach 2:
The patent uses partial action by selectively collecting and presenting only the necessary subset of data required for specific troubleshooting scenarios. Rather than processing all possible data from diverse sources, the digital assistant identifies and focuses on the critical information needed to resolve the particular issue at hand, reducing the difficulty of determining relevance.
4Productivity
If virtual machines are dynamically instantiated and associated with hosts, then service deployment is rapid, but operational maintenance and troubleshooting complexity increases
Solution Approach 1:
The patent implements universality through a single digital assistant platform that handles multiple functions: data collection from diverse sources, correlation analysis across virtual and physical layers, troubleshooting recommendation generation, and knowledge base maintenance. This universal system manages the operational complexity of dynamically instantiated VMs without requiring separate tools for each function.
Solution Approach 2:
The patent applies the nested doll principle by organizing troubleshooting data and relationships in hierarchical layers: physical infrastructure data contains virtualized infrastructure data, which contains VNF data, which contains VM data. This nested structure allows the digital assistant to systematically navigate through multiple levels of abstraction, managing operational complexity through organized hierarchical analysis.
Data Source
AI summary
A system includes one or more processors and a memory. The processor(s) effectuates operations including receiving a query, wherein the query identifies one or more problems in the network. The processor(s) further effectuates operations including retrieving contextual information and problem information, associated with the one or more problems, from a knowledge base and generating a first recommendation list comprising one or more recommendations, wherein each of the one or more recommendations comprises the contextual information or the problem information and at least one course of action. The processor(s) further effectuates operations including receiving a selection of a recommendation from the first recommendation list and updating the knowledge base to include information associated with the selection of the recommendation and generating, in response to a further query, a second recommendation list comprising one or more further recommendations that include further contextual information or further problem information retrieved from the knowledge base.