Network Intent Cluster for Automated Troubleshooting
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Solution Overview
Problem
Current network management and troubleshooting methods are inefficient and require extensive manual effort, leading to increased complexity and difficulty in managing and troubleshooting networks, especially for junior engineers, due to the lack of automated methods for enforcing design rules and best practices, resulting in repetitive problems and prolonged downtime.
Innovation Solution
The Problem Diagnosis Automation System (PDAS) uses Network Intent (NI) and Network Intent Cluster (NIC) to automate the diagnosis and enforcement of preventive measures across the network, enabling automated diagnosis and remediation of repetitive issues through a no-code platform, triggered automation framework, and graphical user interface for incident management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual network troubleshooting methods are used, then network engineers can diagnose and resolve issues, but the process requires extensive time and effort, leading to prolonged downtime
Solution Approach 1:
The system performs preliminary actions by automatically collecting network device data, configurations, and performance metrics continuously before issues occur. This pre-gathering of information enables rapid diagnosis when problems arise, eliminating the need for manual data collection during troubleshooting and significantly reducing resolution time.
Solution Approach 2:
The system implements self-service by automatically diagnosing network issues using pre-configured troubleshooting playbooks and algorithms. When anomalies are detected, the system autonomously analyzes the data, identifies root causes, and executes remediation actions without requiring manual intervention from network engineers, thereby minimizing downtime.
2Productivity
If comprehensive network monitoring and automation are implemented, then troubleshooting efficiency improves, but system complexity increases
Solution Approach 1:
The system applies universality by creating a multi-functional platform that combines network data collection, anomaly detection, root cause analysis, and automated remediation capabilities in a single integrated system. This universal approach handles diverse network devices and protocols through standardized interfaces, improving efficiency without proportionally increasing complexity.
Solution Approach 2:
The system introduces an intermediary layer between network devices and engineers - an automated analysis platform that translates complex network data into actionable insights. This intermediary handles the complexity of data processing and device-specific protocols, presenting simplified information and automated actions to users, thereby improving productivity while containing perceived complexity.
3Loss of time
If automated diagnosis systems are deployed, then mean time to repair decreases, but implementation and maintenance costs increase
Solution Approach 1:
The system implements partial automation by allowing organizations to deploy troubleshooting playbooks incrementally, starting with specific device types or protocols. This phased approach enables MTR reduction in critical areas first, while spreading implementation resources over time. The system can handle both fully automated and manual-execution scenarios, providing flexibility in resource allocation.
Data Source
AI summary
Problem Diagnosis Automation System (PDAS) automates the diagnosis of repetitive problems and the enforcement of preventive measures across a network. Automation assets across the network include Network Intent (NI) inside the no-code platform. A Network Intent Cluster (NIC) clones a NI across the network to create a group of NIs (member NIs) with the same design or logic. A subset of Member NIs can be executed according to user-defined conditions based on the member device, the member NI tags, or signature variables. A Triggered Automation Framework (TAF) matches the incoming API calls from a 3rd party system to current incidents and installs the automation (e.g., NI/NIC) to be triggered for each call. It may include: Integrated IT System defining the scope and data of the incoming API calls; Incident Type to match a call to an Incident; and Triggered Diagnosis to define what and how the NIC/NI is executed.