Abstract:
Systems for rule-based data protection of virtualized computing entities. A method embodiment commences upon receiving specification parameters that correspond to one or more data protection schemes or data protection configurations such as schemes for making backups or snapshots. Based on the specification parameters and the data protection configurations, one or more resource entities associated with the data protection configurations are identified. Each of the resource entities are accessed and one or more applicable data protection administration rules that correspond to the respective resource entities are applied. The resource entities might be organized hierarchically. Determination of which data protection administration rules are applicable to the resource entities is based on the type of resource entity being considered. The combination of the resource entity type and respective applicable rules is used to generate entity-specific data protection commands. The entity-specific commands to administer the data protection configurations over the resource entities are executed.
Abstract:
Systems for managing a distributed computing system. A method embodiment commences upon receiving user-provided specification parameters that describe a target state of a computing entity. After generating a set of resource management commands to achieve the specified target state of a computing entity, the resource management commands are scheduled for execution. As execution of the resource management commands is being carried out, the execution status of the resource management commands is continuously monitored. If performance of the resource management commands raises an error and/or the resource entity state is different than predicted, a set of remediation actions are determined, and an additional set of processing operations are invoked to accomplish the remediation actions. When all resource management commands and/or any remediation actions have completed successfully, the target resource entity state has been achieved. The user did not need to write executable code to perform steps that pursue the target state.
Abstract:
Systems for achieving and maintaining a specified state of a computing resource in a distributed computing environment. A method embodiment commences upon receiving one or more specification parameters that describe a desired target state associated with a particular computing resource and/or of a particular computing environment. The specification parameters that characterize the desired target state of a resource are recorded in a target state data structure. Periodically, an agent issues a state progression query to determine if the computing resource has reached its desired target state. The query is then processed by collecting state parameters that describe the then-current state of the computing resource or environment. The target state data structure is accessed to identify one or more state differences between the desired target state and the then-current state of the particular computing resource and/or its particular computing environment. Remediation operations based on the state differences are then carried out.
Abstract:
A method for dynamically adjusting between asynchronous and synchronous data replication policies in a networked virtualization environment, includes identifying a current data replication policy for a user virtual machine (VM) determining a load level by a source service VM associated with the user VM and calculating a desired data replication policy for the user VM based on at least the load level.
Abstract:
Disclosed is an approach for implementing disaster recovery for virtual machines. Consistency groups are implemented for virtual machines, where the consistency group link together two or more VMs. The consistency group includes any set of VMs which need to be managed on a consistent basis in the event of a disaster recovery scenario.
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. Licenses to the virtualization system software components can float between any combination of different cloud computing infrastructures, including floating the licenses between private clouds and public clouds.
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:
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. The data state is restored by the API using data level restore operations.
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. Messages are sent between the client computing device and the alternative virtualized controller to access one of the storage target devices for performing storage I/O protocols.