Abstract: Methods, systems, and computer-readable storage media for receiving a set of timeseries, each timeseries in the set of timeseries representing a parameter of execution of the system, pre-processing each timeseries in the set of timeseries to provide a set of pre-processed timeseries, merging timeseries in the set of timeseries to provide a merged timeseries, generating a consolidated timeseries based on the merged timeseries and a periodicity, deriving a pattern based on the consolidated time series, the pattern defining a scaling factor for each period in a timeframe, and executing, by an instance manager, scaling of the system based on the pattern to selectively scale one or more of instances of the system and controllable resources based on scaling factors of the pattern.

Abstract: Methods and apparatus are disclosed for representing a dynamic hierarchical system as a composite graph data structure. Members of a system are represented as primary nodes of a tree. Multiple system states have respective trees, which can be overlaid to obtain a composite graph. The composite graph can be augmented with secondary nodes for member attributes, and further nodes for state-dependent values of the attributes. Methods of processing bottom-up, top-down, and filtered queries are disclosed. Applications to military, manufacturing, and communication networks are provided.

Abstract: Methods and apparatus are disclosed for representing a dynamic hierarchical system as a composite graph data structure. Members of a system are represented as primary nodes of a tree. Multiple system states have respective trees, which can be overlaid to obtain a composite graph. The composite graph can be augmented with secondary nodes for member attributes, and further nodes for state-dependent values of the attributes. Methods of processing bottom-up, top-down, and filtered queries are disclosed. Applications to military, manufacturing, and communication networks are provided.