Abstract: The current document is directed to an alternative control interface provided to infrastructure-as-a-service (“IaaS”) clients that provides a comprehensive collection of monitoring and control features to allow IaaS clients to monitor their distributed applications running within execution environments provided by an IaaS and to optimize execution of their distributed applications using the control features provided by the alternative control interface. In addition, the alternative control interface provides process-automation features and services that allow IaaS clients to automate monitoring and control tasks. In the described implementations, the currently disclosed alternative control interface is based on a graph database and a metrics database. The graph database stores a current representation of the infrastructure leased from an IaaS system by an IaaS client and the metrics database continuously collects and stores metric values scraped from components of the infrastructure leased by the IaaS client.

Abstract: The current document is directed to an alternative control interface provided to infrastructure-as-a-service (“IaaS”) clients that provides a comprehensive collection of monitoring and control features to allow IaaS clients to monitor their distributed applications running within execution environments provided by an IaaS and to optimize execution of their distributed applications using the control features provided by the alternative control interface. In addition, the alternative control interface provides process-automation features and services that allow IaaS clients to automate monitoring and control tasks. In the described implementations, the currently disclosed alternative control interface is based on a graph database and a metrics database. The graph database stores a current representation of the infrastructure leased from an IaaS system by an IaaS client and the metrics database continuously collects and stores metric values scraped from components of the infrastructure leased by the IaaS client.

Abstract: The current document is directed to methods and systems that monitor communications through system-component interfaces to detect and block harmful requests and other harmful communications. In a disclosed implementation, a machine-learning-based defender security component is trained, using a minimax-based optimization method similar to that used in generative adversarial networks, to recognize harmful requests and other harmful communications intercepted by the defender from any of various communications paths leading to system-component interfaces, such as a service interface to services provided by a distributed application. The defender passes through harmless messages to their target interfaces and takes various actions with respect to detected harmful messages, including blocking the harmful messages, modifying the harmful messages prior to passing them through to their target interfaces, and other actions.