Abstract: Systems and methods are provided for computationally configuring computing devices and performing multi-layer cluster analysis. For example, the system can identify multiple layers of clusters of devices (e.g., shared hardware configuration, shared application configuration, number of applications, etc.) in a large scale infrastructure environment automatically. For each layer of the clusters of devices, parameters of these devices are provided to a machine learning model to produce an objective function (e.g., minimum number of devices, utilization under 80%, etc.), whose output can be provided to a datacenter operator or other user in the large scale infrastructure environment so they can make further configuration changes to the devices in each cluster.

Abstract: Systems and methods are provided for computationally configuring computing devices and performing multi-layer cluster analysis. For example, the system can identify multiple layers of clusters of devices (e.g., shared hardware configuration, shared application configuration, number of applications, etc.) in a large scale infrastructure environment automatically. For each layer of the clusters of devices, parameters of these devices are provided to a machine learning model to produce an objective function (e.g., minimum number of devices, utilization under 80%, etc.), whose output can be provided to a datacenter operator or other user in the large scale infrastructure environment so they can make further configuration changes to the devices in each cluster.

Abstract: Some examples relate to associating an insight with data. In an example, data may be received. A determination may be made that data type of the data is same as compared to an earlier data. An insight generated from the earlier data may be identified, wherein the insight may represent intermediate or resultant data generated upon processing of the earlier data by an analytics function, and wherein during generation metadata is associated with the insight. An analytics function used for generating the insight may be identified.

Abstract: An example method is provided to maintain state information of a virtual machine in a virtualized computing environment through an externally-triggered approach. The method may comprise detecting, by an external entity, that a first host in a cluster is disconnected from a first network connecting the first host to a distributed storage system accessible by the cluster. The method may also comprise instructing the first host to suspend a virtual machine supported by the first host and to store state information associated with the virtual machine. The method may further comprise selecting a second host from the cluster and instructing the first host to migrate the suspended virtual machine to the second host such that the suspended virtual machine is able to resume from suspension on the second host based on the stored state information.

Abstract: An example method is provided to maintain state information of a virtual machine in a virtualized computing environment through a self-triggered approach. The method may comprise detecting, by a first host from a cluster in the virtualized computing environment, that the first host is disconnected from a network connecting the first host to a distributed storage system accessible by the cluster. The method may also comprise suspending, by the first host, a virtual machine supported by the first host and storing state information associated with the virtual machine. The method may further comprise selecting a second host from the cluster and migrating the suspended virtual machine to the second host such that the suspended virtual machine is able to resume from suspension on the second host based on the stored state information.

Abstract: Example implementations relate to performing computations using provenance data. An example implementation includes storing first lineage data of a first dataset and provenance data of an application operating on the first dataset in a storage system. A computing resource may determine whether second lineage data of a second dataset meets a similarity criterion with the first lineage data of the first dataset. A computation on the second dataset may be performed using the provenance data of the application, and an insight of the second dataset may be generated from the performed computation.

Abstract: Example implementations relate to performing computations using provenance data. An example implementation includes storing first lineage data of a first dataset and provenance data of an application operating on the first dataset in a storage system. A computing resource may determine whether second lineage data of a second dataset meets a similarity criterion with the first lineage data of the first dataset. A computation on the second dataset may be performed using the provenance data of the application, and an insight of the second dataset may be generated from the performed computation.

Abstract: Some examples relate to associating an insight with data. In an example, data may be received. A determination may be made that data type of the data is same as compared to an earlier data. An insight generated from the earlier data may be identified, wherein the insight may represent intermediate or resultant data generated upon processing of the earlier data by an analytics function, and wherein during generation metadata is associated with the insight. An analytics function used for generating the insight may be identified.

Abstract: To update a base stream of objects, an overlay stream of objects that update at least some respective objects in the base stream is created, where the overlay stream includes a reference to the base stream.

Abstract: An example method is provided to maintain state information of a virtual machine in a virtualized computing environment through a self-triggered approach. The method may comprise detecting, by a first host from a cluster in the virtualized computing environment, that the first host is disconnected from a network connecting the first host to a distributed storage system accessible by the cluster. The method may also comprise suspending, by the first host, a virtual machine supported by the first host and storing state information associated with the virtual machine. The method may further comprise selecting a second host from the cluster and migrating the suspended virtual machine to the second host such that the suspended virtual machine is able to resume from suspension on the second host based on the stored state information.

Abstract: An example method is provided to maintain state information of a virtual machine in a virtualized computing environment through an externally-triggered approach. The method may comprise detecting, by an external entity, that a first host in a cluster is disconnected from a first network connecting the first host to a distributed storage system accessible by the cluster. The method may also comprise instructing the first host to suspend a virtual machine supported by the first host and to store state information associated with the virtual machine. The method may further comprise selecting a second host from the cluster and instructing the first host to migrate the suspended virtual machine to the second host such that the suspended virtual machine is able to resume from suspension on the second host based on the stored state information.