Server Cluster Identification via Social Group Analysis
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Solution Overview
Problem
Current methods for identifying server clusters in data centers are laborious, inaccurate, and expensive, often relying on outdated configuration management databases (CMDBs, which can contain mistakes, and require technician knowledge, leading to potential downtime during server relocation due to unknown dependencies.
Innovation Solution
Applying social group analysis to network traffic data to identify server clusters, generating graphical representations that aid in the relocation of servers without relying on CMDBs or technician knowledge, ensuring accuracy and scalability across hundreds or thousands of servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of data center configurations is used to identify server clusters, then technician knowledge and expertise can be applied to understand dependencies, but the process becomes laborious, expensive, and time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection methods with automated network traffic analysis. Instead of technicians manually examining configurations and documentation, the system automatically captures and analyzes network traffic patterns to identify server dependencies and clusters, eliminating the need for human labor in the identification process while maintaining or improving accuracy.
Solution Approach 2:
The system enables the network infrastructure to self-describe its dependencies through actual traffic patterns. Rather than relying on external technician knowledge or static documentation, the servers and network automatically generate the information needed for cluster identification through their own communication patterns, making the system self-documenting and self-analyzing.
2Ease of manufacture
If configuration management databases (CMDBs) are relied upon for server dependency information, then existing documentation can be utilized, but the data may be outdated, contain mistakes, or be incomplete
Solution Approach 1:
The system implements continuous feedback by monitoring actual network traffic in real-time to verify and update server dependency information. This ongoing observation of communication patterns ensures that the dependency data remains current and accurate, automatically correcting any outdated or incorrect information that might exist in static documentation or CMDBs.
Solution Approach 2:
The system performs preliminary analysis of network traffic patterns to proactively identify server clusters and dependencies before relocation operations are needed. This advance preparation creates an accurate, up-to-date map of server relationships that can be immediately utilized when relocation is required, eliminating the need to rely on potentially outdated pre-existing documentation.
3Ease of operation
If traditional server relocation methods are used without knowledge of server dependencies, then relocation can proceed without specialized expertise, but unknown dependencies may cause downtime or functionality loss
Solution Approach 1:
The system performs preliminary identification of server clusters and dependencies before relocation operations begin. By analyzing network traffic patterns in advance, it pre-maps the relationships between servers, applications, and data flows, enabling planners to understand which servers must be relocated together to maintain functionality and minimize downtime.
Solution Approach 2:
The system segments the overall server infrastructure into distinct clusters based on actual communication patterns and dependencies. This segmentation divides the complex data center into manageable groups of interconnected servers, allowing relocation operations to be planned and executed at the cluster level rather than individual server level, simplifying the process while ensuring dependencies are maintained.
Data Source
AI summary
Examples of identifying a cluster of servers and generating a graphical representation of the cluster of servers are disclosed. In one example implementation according to aspects of the present disclosure, a cluster of servers may be identified based on applying a social group analysis to network traffic related to a plurality of interconnected servers. A graphical representation of the identified cluster of servers may be generated.