Abstract: Disclosed techniques include energy management based on modularized energy control using pooling. Operating data is obtained from a plurality of energy modules within an energy storage system. The plurality of energy modules is pooled. One or more operating goals are obtained for the plurality of energy modules. The operating goals can be based on cost, availability, or energy module status. One or more processors are used to analyze the operating data, the one or more operating goals, energy demand, and energy module operating health. The operation of one or more of the plurality of energy modules is controlled based on the analysis. The energy modules can be a pooled homogeneous bank of energy modules. The homogeneous banks can be pooled into heterogeneous energy modules. The pools can include dynamically added energy module peers.

Abstract: Disclosed techniques include power management across point of source to point of load. Energy is obtained from points of energy generation, where data obtained at a time of energy generation includes information on energy and metadata about the energy. Connection is enabled from the points of energy generation to a large-scale energy storage subsystem. Load information is received from points of load, where the points of load are connected to an energy grid. Processors are used to calculate an energy control policy, based on information on the energy, the energy metadata, availability of the large-scale energy storage subsystem, and the load information. Routing of the energy is controlled from the points of energy generation to the points of load based on the energy control policy. The large-scale energy storage subsystem is controlled based on the energy control policy.

Abstract: Disclosed techniques include power management across point of source to point of load. Energy is obtained from points of energy generation, where data obtained at a time of energy generation includes information on energy and metadata about the energy. Connection is enabled from the points of energy generation to a large-scale energy storage subsystem. Load information is received from points of load, where the points of load are connected to an energy grid. Processors are used to calculate an energy control policy, based on information on the energy, the energy metadata, availability of the large-scale energy storage subsystem, and the load information. Routing of the energy is controlled from the points of energy generation to the points of load based on the energy control policy. The large-scale energy storage subsystem is controlled based on the energy control policy.

Abstract: Disclosed techniques include energy management based on modularized energy control using pooling. Operating data is obtained from a plurality of energy modules within an energy storage system. The plurality of energy modules is pooled. One or more operating goals are obtained for the plurality of energy modules. The operating goals can be based on cost, availability, or energy module status. One or more processors are used to analyze the operating data, the one or more operating goals, energy demand, and energy module operating health. The operation of one or more of the plurality of energy modules is controlled based on the analysis. The energy modules can be a pooled homogeneous bank of energy modules. The homogeneous banks can be pooled into heterogeneous energy modules. The pools can include dynamically added energy module peers.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because resources may be deployed in chains, the capacities of resources to perform the certain acts may be defined to be dependent on the capacities of other resources to perform other acts.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because capacities provided by the resources change as the resources start, the control node uses a predicted pending capacity function to evaluate whether resources that are currently starting will be sufficient to satisfy the level of the service when completely started.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, one or more control nodes provide for the efficient and automated allocation and management of resources within the distributed computing system. In order to provide automated allocation and management of computing functions and resources, a control node uses a set of governors. This set of governors includes a set of service governors to govern services used in the distributed computing system. The set of governors also includes a set of resource governors that govern individual resources of the services in the distributed computing system. The service governors and the resource governors may be implemented using finite state machines.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because capacities provided by the resources change as the resources start, the control node uses a predicted pending capacity function to evaluate whether resources that are currently starting will be sufficient to satisfy the level of the service when completely started.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, one or more control nodes provide for the efficient and automated allocation and management of resources within the distributed computing system. In order to provide automated allocation and management of computing functions and resources, a control node uses a set of governors. This set of governors includes a set of service governors to govern services used in the distributed computing system. The set of governors also includes a set of resource governors that govern individual resources of the services in the distributed computing system. The service governors and the resource governors may be implemented using finite state machines.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because capacities provided by the resources change as the resources start, the control node uses a predicted pending capacity function to evaluate whether resources that are currently starting will be sufficient to satisfy the level of the service when completely started.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, one or more control nodes provide for the efficient and automated allocation and management of resources within the distributed computing system. In order to provide automated allocation and management of computing functions and resources, a control node uses a set of governors. This set of governors includes a set of service governors to govern services used in the distributed computing system. The set of governors also includes a set of resource governors that govern individual resources of the services in the distributed computing system. The service governors and the resource governors may be implemented using finite state machines.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, one or more control nodes provide for the efficient and automated allocation and management of resources within the distributed computing system. In order to provide automated allocation and management of computing functions and resources, a control node uses a set of governors. This set of governors includes a set of service governors to govern services used in the distributed computing system. The set of governors also includes a set of resource governors that govern individual resources of the services in the distributed computing system. The service governors and the resource governors may be implemented using finite state machines.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because capacities provided by the resources change as the resources start, the control node uses a predicted pending capacity function to evaluate whether resources that are currently starting will be sufficient to satisfy the level of the service when completely started.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because resources may be deployed in chains, the capacities of resources to perform the certain acts may be defined to be dependent on the capacities of other resources to perform other acts.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, one or more control nodes provide for the efficient and automated allocation and management of resources within the distributed computing system. In order to provide automated allocation and management of computing functions and resources, a control node uses a set of governors. This set of governors includes a set of service governors to govern services used in the distributed computing system. The set of governors also includes a set of resource governors that govern individual resources of the services in the distributed computing system. The service governors and the resource governors may be implemented using finite state machines.

Abstract: In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, one or more control nodes provide for the efficient and automated allocation and management of resources within the distributed computing system. In order to provide automated allocation and management of computing functions and resources, a control node uses a set of governors. This set of governors includes a set of service governors to govern services used in the distributed computing system. The set of governors also includes a set of resource governors that govern individual resources of the services in the distributed computing system. The service governors and the resource governors may be implemented using finite state machines.