Resiliency Agent for Dynamic Network Endpoint Alignment
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Solution Overview
Problem
Current network management systems face challenges in relocating network components and rerouting traffic flows without disrupting endpoint alignment, leading to inefficiencies, limited scalability, and excessive downtime due to complex interdependencies and manual maintenance processes.
Innovation Solution
A resiliency agent that monitors network components, identifies issues, and dynamically reconfigures them to maintain endpoint alignment and quality of service by utilizing a configuration registry that maps upstream and downstream dependencies, enabling automatic recognition and correction of misconfigurations and traffic imbalances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual maintenance processes are used to relocate network components and reroute traffic flows, then network operations can be controlled, but endpoint alignment is disrupted, leading to excessive downtime and reduced efficiency
Solution Approach 1:
The system implements self-service through automated detection and correction mechanisms. The resiliency agent continuously monitors network components and automatically detects misconfigurations, then applies corrective actions without human intervention to restore endpoint alignment and maintain network operations.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring network component operations and using this information to automatically detect issues. The monitoring data feeds back into the system to trigger automated corrective actions, creating a closed-loop control system that maintains endpoint alignment dynamically.
2Adaptability or versatility
If complex interdependencies between network components are managed manually, then configuration control is maintained, but scalability is limited and device complexity increases
Solution Approach 1:
The resiliency agent acts as an intermediary between network components and their configurations. It automatically manages the complex interdependencies by detecting issues and applying corrective configurations, eliminating the need for manual management of component relationships and enabling system scalability.
Solution Approach 2:
The system achieves scalability through self-service automation. The resiliency agent independently manages configuration complexities and interdependencies without requiring manual intervention, allowing the network to scale as components are added or modified automatically.
3Productivity
If dynamic reconfiguration of network components is implemented, then network efficiency and adaptability are enhanced, but the complexity of managing configurations increases
Solution Approach 1:
The system maintains configuration management simplicity through self-service automation. The resiliency agent automatically detects configuration issues and applies corrective actions, enhancing network efficiency without requiring increased complexity in configuration management, as the system handles adjustments autonomously.
Solution Approach 2:
The system uses feedback-driven automated reconfiguration to improve network efficiency. The resiliency agent monitors network performance and component states, then dynamically adjusts configurations based on this feedback, achieving adaptability without manual complexity.
Data Source
AI summary
Various embodiments are generally directed to techniques for dynamic network resiliency, such as by monitoring and controlling the configuration of one or more network components to ensure proper endpoint alignment, for instance. Some embodiments are particularly directed to a tool (e.g., resiliency agent) that can automatically recognize symptoms of issues with a network component, such as autoscaling, latency, traffic spikes, resource utilization spikes, etcetera, and respond appropriately to ensure continued and optimized operation of the network component. In many embodiments, the tool may dynamically reroute endpoint traffic, perform traffic balancing, and/or drive autoscaling to optimize operation of the network component in response to recognizing symptoms.