Abstract: Systems and methods are described for performing at least one of assessing, optimizing and/or controlling the performance of an Anaerobic Digestion (AD) plant. The system comprises a server that provides a user interface for allowing a user to enter user inputs to define various aspects of the AD plant operation and to view results of the simulation; a database for storing the inputs used for the simulation of the anaerobic digestion plant, the inputs including feedstock inputs, AD operational inputs, and simulation criteria; and a simulation engine that can be operated in an at least one of an off-line simulation mode for generating off-line simulator predictions, a near-line simulation mode for optimizing the performance of the system and an online mode for using machine learning to tune the performance of various models used in simulating the operation of the AD plant.

Abstract: A method manages a cluster. A computer system determines a measurement of a process variable for the cluster, wherein the process variable indicates a quality of service for the cluster. The computer system determines a comparison between the measurement of the process variable and a desired value for the process variable. The computer system determines an adjustment to a setpoint for a runtime metric in the cluster based on the comparison, wherein the runtime metric is a control variable for the cluster. The computer system updates the setpoint for the runtime metric in a controller for the cluster using the adjustment, wherein the runtime metric is used by the controller as the control variable to scale a set of resources in the cluster to cause a value of the runtime metric to move towards the setpoint.

Abstract: Objects are managed in a virtual machine. A frequency of access to fields in objects for an application is identified while the application runs in the virtual machine. An object in the objects is split into a hot object and a cold semi-object based on the frequency of access to the fields in the object as identified while the application runs in the virtual machine, wherein cache misses are reduced from splitting objects based of the frequency of access.

Abstract: Objects are managed in a virtual machine. A frequency of access to fields in objects for an application is identified while the application runs in the virtual machine. An object in the objects is split into a hot object and a cold semi-object based on the frequency of access to the fields in the object as identified while the application runs in the virtual machine, wherein cache misses are reduced from splitting objects based of the frequency of access.

Abstract: Systems and methods are described for performing at least one of assessing, optimizing and/or controlling the performance of an Anaerobic Digestion (AD) plant. The system comprises a server that provides a user interface for allowing a user to enter user inputs to define various aspects of the AD plant operation and to view results of the simulation; a database for storing the inputs used for the simulation of the anaerobic digestion plant, the inputs including feedstock inputs, AD operational inputs, and simulation criteria; and a simulation engine that can be operated in an at least one of an off-line simulation mode for generating off-line simulator predictions, a near-line simulation mode for optimizing the performance of the system and an online mode for using machine learning to tune the performance of various models used in simulating the operation of the AD plant.