Methods and systems for identifying application components in distributed computing facilities

Abstract

The current document is directed to automated methods and systems that employ unsupervised-machine-learning approaches as well as rule-based systems to discover distributed applications within distributed-computing environments. These automated methods and systems provide a basis for higher-level distributed-application administration and management tools and subsystems that provide distributed-application-level user interfaces and operations. In one implementation, the currently disclosed methods and systems employ agents within virtual machines that execute routines and programs and that together comprise a distributed application to continuously furnish information about the virtual machines to a pipeline of stream processors that collect and filter the information to provide for periodic application-discovery. The stream processors generate data representations of the processes currently running on the virtual machines and data representations of the communications connections between the virtual machines. An application-discovery subsystem periodically employs these data representations, and additional data derived from them, to identify the different distributed applications running within a distributed-computing facility and to identify tiers of virtual-machine nodes within each identified distributed application. This, in turn, allows the application-discovery subsystem to generate sets of delta changes for the discovered applications after each periodic application discovery.