Software Image Scoring for Network Device Recommendations
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Solution Overview
Problem
Current network management systems lack a mechanism to effectively evaluate and recommend suitable software images for network devices based on specific entity profiles, often relying on the latest version without considering performance, stability, or security needs of individual entities.
Innovation Solution
A cloud-based WAN assurance system evaluates different software images by computing scores based on historical performance criteria, identifying entities with similar profiles and recommending the most suitable software image for installation, taking into account device performance, network connectivity, and application health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the latest software image version is installed on network devices, then new features and capabilities are provided, but system stability and reliability may deteriorate due to unproven performance
Solution Approach 1:
The system performs preliminary actions by collecting historical performance data from multiple entities before making software image recommendations. It pre-evaluates software images by analyzing performance metrics, stability indicators, and security characteristics from similar network environments, allowing administrators to make informed decisions without immediately deploying unproven latest versions.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting performance data from network devices running different software images across multiple entities. This feedback loop enables the system to learn from real-world performance, stability, and security outcomes, then use this information to generate data-driven recommendations that balance new features with proven reliability.
2Productivity
If software images are selected without evaluating entity-specific requirements, then deployment speed is improved, but suitability and performance matching deteriorates
Solution Approach 1:
The system applies local quality by customizing software image recommendations for each entity based on their specific network characteristics, requirements, and historical performance data. Instead of a uniform approach, it analyzes entity profiles including network size, device types, service requirements, and performance metrics to provide tailored recommendations that precisely match local needs.
Solution Approach 2:
The system utilizes parameter changes by evaluating multiple software images across different versions and configurations, then selecting the optimal image based on varying parameters such as performance metrics, stability ratings, security features, and compatibility with specific entity requirements. This allows flexible optimization rather than rigid deployment.
3Measurement precision
If comprehensive historical data from multiple entities is analyzed, then recommendation accuracy is improved, but system complexity and computational requirements worsen
Solution Approach 1:
The system employs copying by creating standardized entity profiles and performance metrics templates that can be replicated across multiple entities. Instead of analyzing each entity's data in complete isolation, it uses standardized frameworks to structure and compare data from multiple sources, reducing the complexity of handling comprehensive historical data while maintaining analysis accuracy.
Data Source
AI summary
Techniques are disclosed for recommending particular versions of a software image for installation on a network device. In one example, a cloud-based Wide-Area Network (WAN) assurance system determines, for a first entity, entities having similar entity profiles as an entity profile of the first entity. The system obtains historical information, such as historical performance information, for network devices of the entities having similar entity profiles as the entity profile of the first entity. The system computes, based on the historical information, software image scores for software images used by the network devices. The system outputs, for display, an indication specifying a recommended software image to install on a first network device of the first entity, the recommended software image selected based on the software image scores for the software images used by the network devices.