Abstract: Example techniques for execution of functions by clusters of computing nodes are described. In an example, if a cluster does not have resources available for executing a function for handling a service request, the cluster may request another cluster for executing the function. A result of execution of the function may be received by the cluster and used for handling the service request.

Abstract: Techniques for refactoring data in a database are disclosed. A database may initially store both relational and non-relational data. A request to access data from a first data element in the database may be received via a relational database API. Upon receiving the request, a determination is made as to whether the first data element has been refactored into relational and non-relational portions. If the first data element has not been refactored, a refactoring operation is carried out by storing relational portions of the first data element into a relational database and the non-relational portions of the first data element into a non-relational database. The refactoring is performed concurrent with providing data to a requesting computing system. Additionally, refactoring information is updated to indicate that the first data element has been refactored.

Abstract: Methods, apparatuses, and computer-program products are disclosed. The method may include receiving computing metadata associated with management of the data at the source data storage environment. The method may include computing a plurality of behavior parameters for the source data storage environment based on the computing metadata. The method may include determining one or more sub-configurations of a data migration plan based on an application of one or more machine learning models to the plurality of behavior parameters for the source data storage environment. The method may include generating the data migration plan based on a combination of the one or more sub-configurations. The method may include performing a data replication process to replicate the data from the source data storage environment to the target data storage environment based at least in part on the data migration plan.

Abstract: Embodiments described herein are generally directed to testing a microservice to determine whether the microservice leaks sensitive information. According to an example, prior to deployment of a microservice within a production environment, a test suite for the microservice is generated based at least in part on a specification of an application programming interface (API) of the microservice defining operations supported by the API and information regarding parameters of each of the operations. The microservice is subjected to the test suite. A potential security leak by the microservice is then detected by analyzing a dataset to which the microservice outputs information, including applying security rules to the dataset.

Abstract: Systems, methods, and computer program products are provided for providing automatic diagnostics of API configuration. The method includes receiving a local module log relating to one or more changes to code of a local device that would affect an API configuration. The method also includes identifying one or more expected local module logs based at least in part on one or more previous local module logs associated with the local device. The method further includes comparing the local module log with the one or more expected local module logs. The comparison determines any differences between the local module log and the one or more expected local module logs. The method still further includes generating an API configuration diagnostic action based on determining a difference between the local module log and the expected local module log(s). The method also includes causing an execution of the API configuration diagnostic action.

Abstract: Techniques are provided for copying data from a source database to a target database in a database replication system which includes a database event mining system, an event interceptor process and an event receptor process. In one aspect, the event interceptor detects a failure in the event receptor and switches to a mode in which it rejects new database events from the database event mining system. The event interceptor can also request that the database event mining system resend the event after a specified delay. The event interceptor can also shut itself down for a specified period of time, then restart and listen for a pairing request from the event receptor. In another aspect, the event receptor can request that the database event mining system send event data from a specified system change number.

Abstract: Embodiments described herein are generally directed to a creation of an HA private cloud gateway based on a two-node HCI cluster with a self-hosted HMS. According to an example, a request to register a private cloud to be supported by on-premises infrastructure is received by a SaaS portal, which causes a base station to discover servers within the on-premises infrastructure. The base station is then instructed to prepare a server as a deployment node for use in connection with creation of a cluster of two HCI nodes of the servers to represent the HA private cloud gateway, including installing a seed HMS on the deployment node. The base station is further instructed to cause the seed HMS to create the cluster, install a self-hosted HMS within the cluster to manage the cluster, register the cluster to the self-hosted HMS, and finally delete the seed HMS from the deployment node.

Abstract: Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.

Abstract: A system to facilitate a container orchestration cloud service platform is described. The system includes a controller to manage Kubernetes cluster life-cycle operations created by each of a plurality of providers. The controller includes one or more processors to execute a controller micro service to discover a provider plugin associated with each of the plurality of providers, and perform the cluster life-cycle operations at a container orchestration platform as a broker for each of a plurality of providers.

Abstract: In some examples, a system may include a processing resource and a memory resource. The memory resource may store machine-readable instructions to cause the processing resource to create a migration plan, defining characteristics of a migration of an application from a native computing data center to a computing cloud of a plurality of distinct candidate computing clouds, based on: a first migration constraint for the application determined from an analysis of historical behavior of the application executed on the native computing data center; a second migration constraint for the application determined from an analysis of administrator cloud migration preferences for the application; and a cloud computing characteristic of each of the plurality of distinct candidate computing clouds.

Abstract: Example implementations relate to a upgrade of a host that hosts application units of a container-based application. According to an example, monitoring is performed to identify new system software component availability for the cluster. When a new system software component is available, a historical workload pattern of the cluster is analyzed to identify an upgrade window for each host of the cluster. When the upgrade window arrives for a host, it is determined whether reconfiguration of an application is to be performed based on a capacity of the cluster. When the determination is affirmative, a reconfiguration option for the application is identified and a configuration of the application is adjusted accordingly. The host may then be drained, removed from the cluster, upgraded, added back into the cluster and any application configuration changes can be reversed.

Abstract: Example implementations relate to migration of a stateful application from a source computing environment to a destination virtualized computing environment by prioritizing migration of data of the application based on a priority map created based on data usage patterns. An instance of the application is installed within the destination environment. The priority map includes priorities for chunks of the data based on historical data access patterns. The data is migrated from a source volume of the source environment to a destination volume of the destination environment on a chunk-by-chunk basis by performing a background data migration process based on the priority map. Usage of the application concurrent with the data migration process is facilitated by abstracting a location of data being operated upon by the application by maintaining migration status for the chunks. The priority map is periodically updated based on observed data access patterns post application migration.

Abstract: Embodiments described herein are generally directed to a creation of an HA private cloud gateway based on a two-node HCI cluster with a self-hosted HMS. According to an example, a request to register a private cloud to be supported by on-premises infrastructure is received by a SaaS portal, which causes a base station to discover servers within the on-premises infrastructure. The base station is then instructed to prepare a server as a deployment node for use in connection with creation of a cluster of two HCI nodes of the servers to represent the HA private cloud gateway, including installing a seed HMS on the deployment node. The base station is further instructed to cause the seed HMS to create the cluster, install a self-hosted HMS within the cluster to manage the cluster, register the cluster to the self-hosted HMS, and finally delete the seed HMS from the deployment node.

Abstract: Embodiments described herein are generally directed to testing a microservice to determine whether the microservice leaks sensitive information. According to an example, prior to deployment of a microservice within a production environment, a test suite for the microservice is generated based at least in part on a specification of an application programming interface (API) of the microservice defining operations supported by the API and information regarding parameters of each of the operations. The microservice is subjected to the test suite. A potential security leak by the microservice is then detected by analyzing a dataset to which the microservice outputs information, including applying security rules to the dataset.

Abstract: Example implementations relate to a upgrade of a host that hosts application units of a container-based application. According to an example, monitoring is performed to identify new system software component availability for the cluster. When a new system software component is available, a historical workload pattern of the cluster is analyzed to identify an upgrade window for each host of the cluster. When the upgrade window arrives for a host, it is determined whether reconfiguration of an application is to be performed based on a capacity of the cluster. When the determination is affirmative, a reconfiguration option for the application is identified and a configuration of the application is adjusted accordingly. The host may then be drained, removed from the cluster, upgraded, added back into the cluster and any application configuration changes can be reversed.

Abstract: Example implementations relate to migration of a stateful application from a source computing environment to a destination virtualized computing environment by prioritizing migration of data of the application based on a priority map created based on data usage patterns. An instance of the application is installed within the destination environment. The priority map includes priorities for chunks of the data based on historical data access patterns. The data is migrated from a source volume of the source environment to a destination volume of the destination environment on a chunk-by-chunk basis by performing a background data migration process based on the priority map. Usage of the application concurrent with the data migration process is facilitated by abstracting a location of data being operated upon by the application by maintaining migration status for the chunks. The priority map is periodically updated based on observed data access patterns post application migration.

Abstract: Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.

Abstract: Example techniques for selection of deployment environments for applications are described. The deployment environments may be container-based deployment environments. In an example, the selection may be performed based on a historical behavior of an application.

Abstract: In some examples, a method includes identifying, via the use of a Representational State Transfer (REST) Application Programming Interface (API) call, a modification of persistent data for a Cloud Management Platform (CMP); storing, in a persistent log for the CMP, information about the data modification, including an operation that modified the data, a component of the CMP that modified the data, and the time of modification; determining, as a result of a failure of the CMP, a restoration point for the CMP based on the persistent log; and restoring the CMP to the determined restoration point using an independent restoration system of the component that modified the data.

Abstract: Example techniques for execution of functions by clusters of computing nodes are described. In an example, if a cluster does not have resources available for executing a function for handling a service request, the cluster may request another cluster for executing the function. A result of execution of the function may be received by the cluster and used for handling the service request.