Abstract: The current document is directed to methods and subsystems that manage submitted code changes for processing by continuous-integration/continuous-delivery/deployment systems. In disclosed implementations, code changes are processed as quickly as possible, when the code changes are flagged as being urgent. Non-urgent code changes are evaluated for the possibility of merging the non-urgent code changes with additional, subsequently submitted code changes in order to more efficiently employ computational resources needed for processing the code changes. When there is a code change, waiting for processing, with which a submitted code change can be merged, the submitted code change is merged with the waiting code change so that the merged code changes can be together verified.

Abstract: Intelligent capacity planning is provided for storage in a hyperconverged infrastructure environment. The storage may be a logical storage unit that is supported by storage space of a plurality of hardware disks in a virtualized computing environment. Failure predictions can be obtained for each individual hardware disk, and a failure prediction for a number of hardware disk in a hardware disk set can also be obtained. A failure prediction and/or a reduced availability prediction for the logical storage unit can be generated based at least on a configuration state of the logical storage unit, a prediction for one or more hardware disks of the logical storage unit, and a prediction time. Predictions based on what-if operations are also able to be generated.

Abstract: Intelligent capacity planning is provided for storage in a hyperconverged infrastructure environment. The storage may be a logical storage unit that is supported by storage space of a plurality of hardware disks in a virtualized computing environment. Failure predictions can be obtained for each individual hardware disk, and a failure prediction for a number of hardware disk in a hardware disk set can also be obtained. A failure prediction and/or a reduced availability prediction for the logical storage unit can be generated based at least on a configuration state of the logical storage unit, a prediction for one or more hardware disks of the logical storage unit, and a prediction time. Predictions based on what-if operations are also able to be generated.

Abstract: Capacity forecasting may be performed for distributed storage resources in a virtualized computing environment. Historical data indicative of usage of the storage resources is transformed into a privacy-preserving format and is preprocessed to remove outliers, to fill in missing values, and to perform normalization. The preprocessed historical data is inputted into a machine-learning model, which applies a piecewise regression to the historical data to generate a prediction output.

Abstract: An analytics portal, having a machine learning model, is deployed at an edge device in a virtualized computing environment. The machine learning model may be trained internally in the virtualized computing environment or via trained models received via an external network such as a cloud. The analytics portal is in an active mode, while another analytics portal at another host or edge device in the virtualized computing environment is in a passive mode. An election process may be used to change an analytics portal from the active mode to the passive mode. A failover process is also available to transition the passive analytics portal to the active mode, in response to a failure of the current active analytics portal.