Abstract: A distributed computing design system facilitates the creation and deployment of complex data and mathematical models. In one implementation, the system generates a graphical user interface of a visual distributed computing design workspace. The visual distributed computing design workspace includes a node palette comprising individually selectable nodes, each with a graphical representation and corresponding to a distributed computing function available on a pre-determined target distributed computing cluster, a linking tool for establishing connection links between the individually selectable nodes, and a digital canvas.

Abstract: A method for provisioning a computer includes providing a graph that defines relationships between one or more hardware components of a plurality of computers and component characteristics of the one or more hardware components, and relationships between one or more applications and requirements of the one or more applications. The method further includes receiving a selection of an application and determining, via the graph, whether at least one computer with hardware components capable of meeting the requirements of the application exists. If a computer exits, the method also includes communicating the application to the computer; triggering the computer to execute the application; and communicating, from the computer, data processed by the application to an external system.

Abstract: A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The machine facilitates building, deploying, and/or training analytical models by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The machine can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The machine also provides a dynamically reconfigurable user interface for controlling the system.

Abstract: A method for provisioning a computer includes providing a graph that defines relationships between one or more hardware components of a plurality of computers and component characteristics of the one or more hardware components, and relationships between one or more applications and requirements of the one or more applications. The method further includes receiving a selection of an application and determining, via the graph, whether at least one computer with hardware components capable of meeting the requirements of the application exists. If a computer exits, the method also includes communicating the application to the computer; triggering the computer to execute the application; and communicating, from the computer, data processed by the application to an external system.

Abstract: A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The system facilitates building, deploying, and/or training analytical models, by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The system can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The system also provides a dynamically reconfigurable user interface for controlling the system.

Abstract: A machine provides a system and interface to deploy and manage pre-defined analytical models across various compute engines or run time environments, e.g., by exposing analytical model deployment and management parameters to a user while abstracting model deployment activities. The machine also determines proper run time environments for the pre-defined analytical model and verifies the pre-defined analytical model. The machine also provides a dynamically reconfigurable user interface for controlling the machine.

Abstract: A method for provisioning a computer includes providing a graph that defines relationships between one or more hardware components of a plurality of computers and component characteristics of the one or more hardware components, and relationships between one or more applications and requirements of the one or more applications. The method further includes receiving a selection of an application and determining, via the graph, whether at least one computer with hardware components capable of meeting the requirements of the application exists. If a computer exits, the method also includes communicating the application to the computer; triggering the computer to execute the application; and communicating, from the computer, data processed by the application to an external system.

Abstract: The differentiated service-based graceful degradation layer (DSGDL) allows cloud-based architectures to operate through and recover from periods of limited capability. The DSGDL protects and continues serving higher priority requests with the best possible response even as the underlying cloud-based services deteriorate. The DSGDL offloads lower priority requests to lower-grade secondary capability that can be dynamically provisioned in order to reserve the best capability for maintaining high priority service (e.g., by re-directing lower priority requests to a slightly out-of-date cached dataset, and reserve the primary consistent database for higher priority requests). The DSGDL 1) implements an overlay network over existing cloud services to route and enforce priority requests, and 2) provisions on-demand computing nodes and sites to provide secondary capability for service requests as needed.

Abstract: The distributed computing backup and recovery (DCBR) system and method provide backup and recovery for distributed computing models (e.g., NoSQL). The DCBR system extends the protections from server node-level failure and introduces persistence in time so that the evolving data set may be stored and recovered to a past point in time. The DCBR system, instead of performing backup and recovery for an entire dataset, may be configured to apply to a subset of data. Instead of keeping or recovering snapshots of the entire dataset which requires the entire cluster, the DCBR system identifies the particular nodes and/or archive files where the dataset resides so that backup or recovery may be done with a much smaller number of nodes.

Abstract: A method for provisioning a computer includes providing a graph that defines relationships between one or more hardware components of a plurality of computers and component characteristics of the one or more hardware components, and relationships between one or more applications and requirements of the one or more applications. The method further includes receiving a selection of an application and determining, via the graph, whether at least one computer with hardware components capable of meeting the requirements of the application exists. If a computer exits, the method also includes communicating the application to the computer; triggering the computer to execute the application; and communicating, from the computer, data processed by the application to an external system.

Abstract: A cloud services monitoring system (CSMS) helps a consumer of cloud computing resources determine the state of the cloud computing resources available to the consumer. The CSMS is particularly effective for cloud models where the consumer of the cloud computing resources is not the same as the provider of the cloud resource. The CSMS also provides mechanisms for adjusting operations actions to preserve service guarantees for the consumer. In that regard, the CSMS dynamically adjusts operations actions in response to the state of the cloud, rather than executing fixed predetermined operations actions that are not tailored to the monitored conditions.

Abstract: A distributed computing design system facilitates the creation and deployment of complex data and mathematical models. In one implementation, the system generates a graphical user interface of a visual distributed computing design workspace. The visual distributed computing design workspace includes a node palette comprising individually selectable nodes, each with a graphical representation and corresponding to a distributed computing function available on a pre-determined target distributed computing cluster, a linking tool for establishing connection links between the individually selectable nodes, and a digital canvas.

Abstract: API analytics systems and methods are provided. Frequently occurring API usage patterns may be determined from API call data collected from across an API ecosystem. Alternatively or in addition, a classification structure, such as a decision tree, may be generated from the API usage patterns. A type of activity that resulted in a set of API calls being invoked may be determined from the classification structure. A similarity or difference between the set of API calls and the frequently occurring API usage patterns may also be determined and/or identified.

Abstract: A machine provides a system and interface to deploy and manage pre-defined analytical models across various compute engines or run time environments, e.g., by exposing analytical model deployment and management parameters to a user while abstracting model deployment activities. The machine also determines proper run time environments for the pre-defined analytical model and verifies the pre-defined analytical model. The machine also provides a dynamically reconfigurable user interface for controlling the machine.

Abstract: A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The machine facilitates building, deploying, and/or training analytical models by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The machine can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The machine also provides a dynamically reconfigurable user interface for controlling the system.

Abstract: A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The system facilitates building, deploying, and/or training analytical models, by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The system can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The system also provides a dynamically reconfigurable user interface for controlling the system.

Abstract: According to an example, an adaptive fault diagnosis system may include a memory storing machine readable instructions to receive metrics and events from an enterprise system, and use a substitution graph to determine if a received metric or a received event belongs to a cluster that includes one or more correlated metrics and/or events grouped based on similarity. If the received metric or the received event belongs to the cluster, the memory may further store machine readable instructions to use a detection graph to determine if the received metric or the received event is identifiable to form a fault pattern by traversing a fault path of the detection graph. Further, the memory may further store machine readable instructions to diagnose a fault based on the traversal of the fault path of the detection graph. The system may include a processor to implement the machine readable instructions.

Abstract: The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes (e.g., consolidation strategies) usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads.

Abstract: The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes (e.g., consolidation strategies) usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads.

Abstract: According to an example, an adaptive fault diagnosis system may include a memory storing machine readable instructions to receive metrics and events from an enterprise system, and use a substitution graph to determine if a received metric or a received event belongs to a cluster that includes one or more correlated metrics and/or events grouped based on similarity. If the received metric or the received event belongs to the cluster, the memory may further store machine readable instructions to use a detection graph to determine if the received metric or the received event is identifiable to form a fault pattern by traversing a fault path of the detection graph. Further, the memory may further store machine readable instructions to diagnose a fault based on the traversal of the fault path of the detection graph. The system may include a processor to implement the machine readable instructions.