Automonous digital twin generation using edge-nodes
Apparatus and methods for building a digital twin of a datacenter using edge-node computing is provided. Edge-nodes may collect telemetry data points from different infrastructure components of the datacenter. An AI engine may be configured to create a digital twin of the datacenter based on the data points captured by the edge-nodes. The digital twin may be segmented into a plurality of layers, and each layer may represent a different logical layer of the datacenter. Utilizing edge-nodes to capture data points may prevent overloading components of the datacenter and generate a digital twin for any suitable layer (physical or virtual) of datacenter.
1 . An artificial intelligence (“AI”) system for building a component level digital twin of a datacenter, the system comprising:
a plurality of edge-nodes that:
capture data traffic from one or more infrastructure components of the datacenter; and
transmit the captured data traffic to an AI engine; and
based on the captured data traffic, the AI engine is configured to build a digital component twin for each of the one or more infrastructure components, said digital component twin that replicates the one or more infrastructure components, said AI engine being configured to build the digital component twin by:
identifying usage patterns of the digital component twin installed in the datacenter, said usage patterns being derived from the data traffic; and
generating a model that reflects usage of the digital component twin within the datacenter;
wherein the one or more infrastructure components comprise:
a storage system of the datacenter;
a computer server system of the datacenter; and
a communication system of the datacenter.
2 . The AI system of claim 1 wherein the one or more infrastructure components comprise:
an electrical power system of the datacenter;
an environmental control system of the datacenter; and
a data security system of the datacenter.
3 . The AI system of claim 1 , wherein each of the plurality of edge-nodes is configured to filter captured data traffic before transmitting the captured data traffic to the AI engine.
4 . The AI system of claim 1 , wherein each of the plurality of edge-nodes is configured to convert captured data traffic into a common format before transmitting the captured data traffic to the AI engine.
5 . The AI system of claim 1 , wherein the digital component twin is a first digital component twin, the AI engine is further configured to build at least one asset twin that simulates interaction of the first digital component twin and a second digital component twin.
6 . The AI system of claim 5 wherein the AI engine is configured to build the at least one asset twin simulates a containerization process within the datacenter.
7 . The AI system of claim 6 wherein the containerization process simulates provision of a service provided by the datacenter.
8 . The AI system of claim 5 wherein the AI engine is configured to build the at least one asset twin simulates a load balancing virtual application delivery controller.