Clustering tenants based on tenancy knowledge graph

A system comprising:

• one or more processors;
• and memory coupled to the one or more processors comprising instructions causing the one or more processors to perform a method comprising:
• constructing a tenancy knowledge graph comprising a plurality of tenant nodes representing respective tenants in a multitenant computing environment comprising a plurality of host machines, a plurality of property nodes representing respective properties of the tenants, and a plurality of edges connecting the plurality of tenant nodes and the plurality of property nodes, wherein the plurality of edges represent relationship between the plurality of tenant nodes and the plurality of property nodes;
• transforming the plurality of property nodes to corresponding property vectors;
• performing random walks starting from the plurality of tenant nodes of the tenancy knowledge graph;
• feeding sequences of nodes traversed by the random walks into a neural network to generate a plurality of tenant vectors corresponding to the plurality of tenant nodes;
• clustering the plurality of tenant nodes into one or more tenant clusters based on similarity of the plurality of tenant vectors;
• and assigning tenants in the multitenant computing environment based at least in part on the one or more tenant clusters.

One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method comprising:

• constructing a tenancy knowledge graph comprising a plurality of tenant nodes representing respective tenants in a multitenant computing environment comprising a plurality of host machines, a plurality of property nodes representing respective properties of the tenants, and a plurality of edges connecting the plurality of tenant nodes and the plurality of property nodes, wherein the plurality of edges represent relationship between the plurality of tenant nodes and the plurality of property nodes;
• transforming the plurality of property nodes to corresponding property vectors, wherein the properties of tenants comprises issues experienced by the tenants, performance metrics exhibited by the tenants, and workloads incurred by the tenants;
• performing random walks starting from the plurality of tenant nodes of the tenancy knowledge graph;
• feeding sequences of nodes traversed by the random walks into a neural network to generate a plurality of tenant vectors corresponding to the plurality of tenant nodes;
• clustering the plurality of tenant nodes into one or more tenant clusters based on similarity of the plurality of tenant vectors;
• and assigning tenants in the multitenant computing environment based at least in part on the one or more tenant clusters.