REALTIME PROPERTY BASED APPLICATION DISCOVERY AND CLUSTERING WITHIN COMPUTING ENVIRONMENTS
A property-based application discovery. Generating a first application member properties graph based on first discovery information. Generating a second application member properties graph based on second discovery information. Creating a distance matrix based upon the first application member properties graph and the second application member properties graph. Performing a dimension reduction operation on the distanced matrix to obtain a reduced similarity matrix. Performing a property based application discovery operation using the reduced similarity matrix to obtain a reduced output of clustered applications.
1 . An real-time property-based application discovery method in a computing environment, said method comprising:
Generating a first application member properties graph based on a set of properties of the applications; defining a distance matrix between applications;
Generating a second application member propertiesgraph based on second discovery information;
Creating a distance matrix based upon the first application communication graph and the second member properties graph;
performing a dimension reduction operation on the distanced matrix to obtain a reduced similarity matrix;
performing a confidence operation on the reduced similarity matrix; and
generating a cluster of application obtained from the reduced similarity matrix.
2 . The method of claim 1 , wherein said first member properties graph is generated using a density-based spatial density clustering of applications with noise.
3 . The method of claim 1 , wherein said second member properties graph is generated using a density-based spatial density clustering of application with noise.
4 . The method of claim 1 , wherein said first member properties graph is generated using inputs selected from the group consisting of: application name, application hostname, resident host/hypervisor of application, cluster to which application belongs, data center to which application belongs and folder of the application and tier discovery information.
5 . The method of claim 1 , wherein said second member properties graph is generated using inputs selected from the group consisting of: application name, application hostname, resident host/hypervisor of application, cluster to which application belongs, data center to which application belongs and folder of the application and tier discovery information.
6 . The method of claim 1 , wherein said similarity matrix is based upon a distance between application farthest point with a cluster.
7 . The method of claim 1 , wherein said creating a similarity matrix further comprises:
creating a first similarity matrix corresponding to said first application member properties graph.
8 . The method of claim 7 , wherein said creating a similarity matrix further comprises:
creating a second similarity matrix corresponding to said second application member properties graph.
9 . The method of claim 8 , wherein said creating a similarity matrix further comprises:
computing an elbow of the distances corresponding to the first similarity and the second similarity to obtain a best value of the cluster.
10 . The method of claim 8 , wherein said obtain a best value of the cluster further comprises:
sorting all the calculated distances of the applications in ascending order.
11 . The method of claim 10 , further comprises computing a nearest neighbor from a given input feature matrix for each cluster.
12 . A computer-implemented method for performing a real-time property-based application discovery in a virtual environment, said computer-implemented method comprising:
generating a first application member properties graph; said first application member properties graph based on discovered property information from said virtual environment;
generating a second application member properties graph; said second application member properties graph based on discovered property information from said virtual environment;
creating a similarity matrix based upon distance calculation points said first application member properties graph and said second application member properties graph;
performing a dimension reduction operation on said similarity matrix to obtain a reduced similarity matrix;
performing a best range operation on the similarity reduced matrix to obtain a reduced output; and
generating a cluster of applications from the reduced similarity matrix reduced output.
13 . The computer-implemented of claim 12 , wherein said first member properties graph is generated using a density based spatial density clustering of applications with noise.
14 . The computer-implemented of claim 12 , wherein said second member properties graph is generated using a density based spatial density clustering of applications with noise.
15 . The computer-implemented of claim 12 , wherein said first member properties graph is generated using inputs selected from the group consisting of: flows, endpoints, application and tier discovery information.
16 . The computer-implemented of claim 12 , wherein said second member properties graph is generated using inputs selected from the group consisting of: application name, application hostname, resident host/hypervisor of application, cluster to which an application belongs and folder of the application.
17 . The computer-implemented of claim 12 , wherein said similarity matrix is based upon computing the distance to the farthest point of the application within its cluster.
18 . The computer-implemented of claim 12 , wherein said creating a similarity matrix further comprises:
creating a first similarity matrix corresponding to said first application member properties graph.
19 . The computer-implemented of claim 17 , wherein said creating a similarity matrix further comprises:
creating a second similarity matrix corresponding to said second application member properties graph.
20 . The computer-implemented of claim 18 , wherein said creating a similarity matrix further comprises:
computing an elbow along said member properties graphs of a series of farthest points.