Abstract: Disclosed is an approach to implement new behavior using natural language, and to debug and examine what happened in the past via a natural language interface as well. Some approaches use a combination of natural language understanding techniques, knowledge representation techniques, advanced compiler techniques and user interaction techniques, to solve for natural language programming and debugging of computers.

Abstract: Methods, systems and computer program products for bringing-up a computing cluster on a public cloud infrastructure with techniques utilizing expressed intents (high level descriptions of desired configuration) and asynchronously receiving configuration status messages from the public cloud infrastructure. The method includes a cloud management computing system transmitting to the public cloud infrastructure a first expressed intent for bringing-up a computing cluster. The cloud management computing system asynchronously receiving periodic status messages comprising cluster status data from the public cloud infrastructure reflecting a current configuration state of the computing cluster. The system determines, based on the cluster status data, whether the first expressed intent for the computing cluster has been achieved.

Abstract: Methods, systems, and computer program products for flexible virtualization system deployment into different cloud computing environments. A set of floating licenses to virtualization system software components is established. The set of floating licenses are configured to permit usage of the virtualization system software components on different cloud computing infrastructures. Workload parameters of a workload to be deployed to one of the different cloud computing infrastructures is considered with respect to cloud attributes corresponding to the different cloud computing infrastructures. One or more candidate target cloud computing infrastructures are selected based upon a comparison between workload attributes of a computing workload and cloud attributes of the candidate target cloud computing infrastructures. Virtualization system software components are deployed into the selected target cloud computing infrastructures.

Abstract: Disclosed is an improved approach to implement software programming which allows for interactive coding without having to restart a procedure. This is implemented using a combination of an interpreter which allows for modification of code during runtime using an interactive console and a versioned memory system used by the interpreter to roll-back memory to a previous state of execution.

Abstract: Upon receiving a request to hibernate a hypervisor of a virtualization system running on a first computer, acts are carried out to capture a state of the hypervisor, where the state of the hypervisor comprises hypervisor logical resource parameters and an execution state of the hypervisor. After hibernating the hypervisor by quiescing the hypervisor and storing the state of the hypervisor into a data structure, the data structure is moved to a different location. At a later moment in time, the data structure is loaded onto a second computing machine and restored. The restore operation restores the hypervisor and all of its state, including all of the virtual machines of the hypervisor as well as all of the virtual disks and other virtual devices of the virtual machines. Differences between the first computing machine and the second computing machine are reconciled before execution of the hypervisor on the second machine.

Abstract: A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.

Abstract: Examples of systems are described herein which may dynamically allocate compute resources to recovery clusters. Accordingly, a recovery site may utilize fewer compute resources in maintaining recovery clusters for multiple associate clusters, while ensuring that, during use, compute resources are allocated to a particular cluster. This may reduce and/or avoid vulnerabilities arising from a use of shared resources in a virtualized and/or cloud environment.

Abstract: Methods, systems, and computer program products for flexible virtualization system deployment into different cloud computing environments. A set of floating licenses to virtualization system software components is established. The set of floating licenses are configured to permit usage of the virtualization system software components on different cloud computing infrastructures. Workload parameters of a workload to be deployed to one of the different cloud computing infrastructures is considered with respect to cloud attributes corresponding to the different cloud computing infrastructures. One or more candidate target cloud computing infrastructures are selected based upon a comparison between workload attributes of a computing workload and cloud attributes of the candidate target cloud computing infrastructures. Virtualization system software components are deployed into the selected target cloud computing infrastructures.

Abstract: Upon receiving a request to hibernate a hypervisor of a virtualization system running on a first computer, acts are carried out to capture a state of the hypervisor, where the state of the hypervisor comprises hypervisor logical resource parameters and an execution state of the hypervisor. After hibernating the hypervisor by quiescing the hypervisor and storing the state of the hypervisor into a data structure, the data structure is moved to a different location. At a later moment in time, the data structure is loaded onto a second computing machine and restored. The restore operation restores the hypervisor and all of its state, including all of the virtual machines of the hypervisor as well as all of the virtual disks and other virtual devices of the virtual machines. Differences between the first computing machine and the second computing machine are reconciled before execution of the hypervisor on the second machine.

Abstract: Examples of systems are described herein which may dynamically allocate compute resources to recovery clusters. Accordingly, a recovery site may utilize fewer compute resources in maintaining recovery clusters for multiple associate clusters, while ensuring that, during use, compute resources are allocated to a particular cluster. This may reduce and/or avoid vulnerabilities arising from a use of shared resources in a virtualized and/or cloud environment.

Abstract: Methods, systems and computer program products for bringing-up a computing cluster on a public cloud infrastructure with techniques utilizing expressed intents (high level descriptions of desired configuration) and asynchronously receiving configuration status messages from the public cloud infrastructure. The method includes a cloud management computing system transmitting to the public cloud infrastructure a first expressed intent for bringing-up a computing cluster. The cloud management computing system asynchronously receiving periodic status messages comprising cluster status data from the public cloud infrastructure reflecting a current configuration state of the computing cluster. The system determines, based on the cluster status data, whether the first expressed intent for the computing cluster has been achieved.

Abstract: Systems for high-performance computing. A storage control architecture is implemented by a plurality of nodes, where a node comprises combinations of executable containers that execute in cooperation with virtual machines running above a hypervisor. The containers run in a virtual machine above a hypervisor, and/or can be integrated directly into the operating system of a host node. Sensitive information such as credit card information may be isolated from the containers in a separate virtual machine that is configured to be threat resistant, and which can be accessed through a threat resistant interface module. One of the virtual machines of the node may be a node-specific control virtual machine that is configured to operate as a dedicated storage controller for a node. One of the virtual machines of the node may be a node-specific container service machine that is configured to provide storage-related and other support to a hosted executable container.

Abstract: Methods, systems, and computer program products for flexible virtualization system deployment into different cloud computing environments. A set of floating licenses to virtualization system software components is established. The set of floating licenses are configured to permit usage of the virtualization system software components on different cloud computing infrastructures. Workload parameters of a workload to be deployed to one of the different cloud computing infrastructures is considered with respect to cloud attributes corresponding to the different cloud computing infrastructures. One or more candidate target cloud computing infrastructures are selected based upon a comparison between workload attributes of a computing workload and cloud attributes of the candidate target cloud computing infrastructures. Virtualization system software components are deployed into the selected target cloud computing infrastructures.

Abstract: Systems and methods for unified application-level backup and restore using heterogeneous cloud-based backup service providers. An application programming interface is configured to process both data level replication operations as well as application-level operations that are executed to carry out high-level commands between a virtualized computing environment and any one or more of the heterogeneous cloud-based backup service providers. The API receives commands from applications in the virtualized computing environment. The API processes commands from the applications so as to facilitate replication of data to selected one or more cloud-based backup service providers. The commands perform data level replication operations as well as application-level operations for storing content to the cloud-based service provider. After a failure event and/or upon receipt of a restore command, the API initiates application-level operations that restore the application and its constituent entities.

Abstract: Methods, systems and non-transitory computer readable media for handling IP network addresses in a virtualization system. Embodiments are configured to receive, from a cloud provider, a cloud provider media access control address to assign to a network interface of a computing node. Also received from the cloud provider is a cloud provider's IP address associated with the cloud provider's media access control address. A virtual machine on the computing node is configured such that the cloud provider's IP address serves as an IP address of the virtual machine and such that a virtualization system media access control address serves as a MAC address of the virtual machine. The virtualization system correlates the cloud provider's media access control address to the IP address of the virtual machine. In some cases, the cloud provider's media access control address stored in a virtual switch of a hypervisor.

Abstract: Systems for high-performance computing. A storage control architecture is implemented by a plurality of nodes, where a node comprises combinations of executable containers that execute in cooperation with virtual machines running above a hypervisor. The containers run in a virtual machine above a hypervisor, and/or can be integrated directly into the operating system of a host node. Sensitive information such as credit card information may be isolated from the containers in a separate virtual machine that is configured to be threat resistant, and which can be accessed through a threat resistant interface module. One of the virtual machines of the node may be a node-specific control virtual machine that is configured to operate as a dedicated storage controller for a node. One of the virtual machines of the node may be a node-specific container service machine that is configured to provide storage-related and other support to a hosted executable container.

Abstract: Disclosed is an approach to implement new behavior using natural language, and to debug and examine what happened in the past via a natural language interface as well. Some approaches use a combination of natural language understanding techniques, knowledge representation techniques, advanced compiler techniques and user interaction techniques, to solve for natural language programming and debugging of computers.

Abstract: Methods, systems, and computer program products for flexible virtualization system deployment into different cloud computing environments. A set of floating licenses to virtualization system software components is established. The set of floating licenses are configured to permit usage of the virtualization system software components on different cloud computing infrastructures. Workload parameters of a workload to be deployed to one of the different cloud computing infrastructures is considered with respect to cloud attributes corresponding to the different cloud computing infrastructures. One or more candidate target cloud computing infrastructures are selected based upon a comparison between workload attributes of a computing workload and cloud attributes of the candidate target cloud computing infrastructures. Virtualization system software components are deployed into the selected target cloud computing infrastructures.

Abstract: Systems and methods for rebalancing storage-oriented workloads across resources of a distributed data storage facility. Communications are initiated between a client computing device and a plurality of storage target devices of a distributed storage system. The distributed storage system comprises one or more computing nodes that can host virtualized controllers. A client computing device interfaced with the distributed storage system uses an IP address to access an initial virtualized controller. Upon detecting a change event, conditions of the computing environment are analyzed to determine rebalancing options. Analysis of the rebalancing options results in identification of an alternative virtualized controller. A redirect message that identifies the alternative virtualized controller is sent to the computing device. The client computing device connects to the alternative virtualized controller.

Abstract: Examples of systems are described herein which may dynamically allocate compute resources to recovery dusters. Accordingly, a recovery site may utilize fewer compute resources in maintaining recovery clusters for multiple associate clusters, while ensuring that, during use, compute resources are allocated to a particular cluster. This may reduce and/or avoid vulnerabilities arising from a use of shared resources in a virtualized and/or cloud environment.