Discovering configuration templates of virtualized tenant networks in multi-tenancy datacenters via graph-mining

Multi-tenant datacenter networking, with which multiple customer (tenant) networks are virtualized over a single shared physical infrastructure, is cost-effective but poses significant costs on manual configuration. Such tasks would be alleviated with configuration templates, whereas a crucial difficulty stems from creating appropriate (i.e., reusable) ones. In this work, we propose a graph-based method of mining configurations of existing tenants to extract their recurrent patterns that would be used as reusable templates for upcoming tenants. The effectiveness of the proposed method is demonstrated with actual configuration files obtained from a business datacenter network.