Provided in an example is a cloud computing service catalog which enables a cloud computing user to prepare and implement a blueprint of service items across multiple different clouds of computing resources. An example method includes discovering a plurality of computer resources which are available from at least one of a first platform and a second platform, and receiving data describing a plurality of service items. The service items include instructions configured to control one or more tangible computing devices. A first service item in the plurality of service items corresponds to a first computer resource in the first platform. A second service item in the plurality of service items corresponds to a second computer resource in the second platform. The method also includes adding the service items to the blueprint.

A method for issuing commands to remote devices comprising determining a criterion that forms a rule for a service, the service comprising a service property, a service method, and a service event distributing the rule to a behavior engine on a programmable device, the behavior engine comprising a set of rules, and evaluating, at the behavior engine, if a trigger criterion for the rule is met. Upon determining that the trigger criterion is met, the method may further comprise performing an action comprising evaluating, at the behavior engine, if a condition is met, and upon determining that the condition is met, issuing a command to perform a first action comprising setting a service property and calling a service method for all devices including the service property within a scope of the action, defining an action scope.

A system maintains, generates, and manages infrastructure layouts. The infrastructure layouts interconnect infrastructure components and capture relational aspects between the components within the interconnections. The infrastructure layouts map northbound services, which are service outputs, to southbound services, which are service capabilities, for fulfilment. The system may traverse a mapping from a northbound service to a fulfilling southbound service to generate a workflow to support deployment of the northbound service. In various implementations, the system may compare a path, which maps a northbound service to a southbound service, to a policy model to determine compliance with the policy.

Embodiments configure a cloud system that includes a plurality of cloud services. Embodiments receive a user utterance that includes a natural language and extract at least a first entity from the utterance. Embodiments translate the first entity into a cloud intent definition language entity and receive user feedback in response to presenting the cloud intent definition language entity. Embodiments generate an intent based on the cloud intent definition language entity and the feedback and compile the intent into a cloud services policy to be deployed by the cloud system.

Embodiments of the present invention provide a method, system and computer program product for flexible provisioning of network resources through a network exposure function (NEF) of a software defined network (SDN) architected mobile communications network. In a flexible provisioning method different instances of correspondingly different virtual network functions (VNFs) are instantiated in a control plane of the SDN and selected ones collected in respectively different collections of a collections pool within the control plane of the SDN. Subsequent to the instantiation, a request by a mobile application can be received in the NEF to access functionality of at least one of the VNFs. In response, one of the different collections of the pool may be selected for assignment to the requesting mobile application and the selected one of the collections then may be assigned to the mobile application through the NEF.

A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

A network slice managing entity, comprising means configured to obtain a service request for a network slice, the service request defining service requirements; request network slice descriptions from a database and selecting at least one network slice which support said service requirements according to the descriptions; generate a resource graph comprising nodes representing infrastructure resources and links representing connections between the nodes, the resource graph depicting resources needed for providing the service, wherein the generating is carried out based on a network slice description of the selected at least one network slice; transmit the resource graph to an infrastructure manager for mapping a topology of said network infrastructure onto said resource graph and determining at least one subset of said network infrastructure; and, receive a mapping result from said infrastructure manager containing said at least one subset of said network infrastructure.

An order is received indicating a network service model. A context of the order is identified. A deployment plan is generated using the network service model, the deployment plan facilitating an instantiation of a contextually-motivated network service instance as a set of normalized lifecycle management (LCM) operations performed against each of a plurality of associated service entities. The deployment plan is deployed, the deploying including binding each of the normalized LCM operations, based on the context of the order, to one or more respective micro-capabilities, each of the respective micro-capabilities having previously been onboarded to the system as one or more corresponding modeled objects capable of being declaratively composed, each of the corresponding modeled objects including a mapping of object properties, object behaviors, and standard LCM operations to one or more existing micro-capabilities of the system. The deploying also including managing execution of the one or more respective micro-capabilities and associated resources, associated storage, and associated network and service allocation and configuration, to instantiate the contextually-motivated network service instance.

A method for determining a misconfiguration of components in an Information Technology (IT) infrastructure includes decomposing one or more components into sub parts, creating one or more synthetic objects, each synthetic object being associated with a sub part of a respective component, and including the components and the synthetic objects in a model of the IT infrastructure. The method further determines a relationship between a first component and a first synthetic object based on attributes of the first component and attributes of the first synthetic object, includes the determined relationship in the model of the IT infrastructure, and loads a graph of the IT infrastructure in a graph database with the first component and the synthetic object as nodes and the determined relationship as an edge in the graph. The method further determines the misconfiguration of components in the IT infrastructure by identifying components having improper relationships in the graph.

Stabilizing a container-based application includes determining a health of a container. Based on the container health, a most recent stable version of an image for the container is identified. A container image is considered stable if containers spawned from the image have a relatively high MTTF and relatively low MTTR compared to other versions of same image. The container is then deployed using the most recent stable version of the image for the container.