[Problem] A lead time for providing a network service can be shortened. [Solution] A network service management apparatus 100 that uses resources included in a network functions virtualization infrastructure 140 to provide a network service includes an orchestrator 110 that defines resources that satisfy a resource requirement of a virtual network function constituting the network service and are allocated to the virtual network function, and reserves the resources, and a virtualized infrastructure manager 130 that secures the reserved resources, activates the virtual network function on the secured resources, and generates the network service. When the securing of the reserved resources fails, the orchestrator 110 re-reserves resources to replace the reserved resources, and the virtualized infrastructure manager 130 secures the re-reserved resources, and activates the virtual network functions on the secured resources.

“Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

“Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

A network allocation vector setting method including: detecting each channel of n channels to determine m occupied busy channels of the n channels and a time duration in which each busy channel is occupied; and setting network allocation vectors of at least m busy channels of the n channels according to l time durations.

A network allocation vector setting method including: detecting each channel of n channels to determine m occupied busy channels of the n channels and a time duration in which each busy channel is occupied; and setting network allocation vectors of at least m busy channels of the n channels according to l time durations.

Resource allocation limitations include resource limits and resource guarantees. A consumer is vulnerable to interruption by other consumers if using more resources than guaranteed. Resources are designated and/or assigned to consumers based on resource limits and resource guarantees. A constraint programming (CP) solver determines resource limits and resource guarantees that minimize vulnerability and/or vulnerability cost based on resource usage data. A CP data model includes limit elements, guarantee elements, and vulnerability elements. The CP data model further includes guarantee-vulnerability constraints, which relies on exceedance distributions generated from resource usage data for the consumers. The CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Novel tools and techniques are provided for implementing intent-based orchestration using network parsimony trees. In various embodiments, in response to receiving a request for network services that comprises desired characteristics and performance parameters for the requested network services without information regarding specific hardware, hardware type, location, or network, a computing system might generate a request-based parsimony tree based on the desired characteristics and performance parameters. The computing system might access, from a datastore, a plurality of network-based parsimony trees that are each generated based on measured network metrics, might compare the request-based parsimony tree with each of one or more network-based parsimony trees to determine a fitness score for each network-based parsimony tree, and might identify a best-fit network-based parsimony tree based on the fitness scores. The computing system might identify and might allocate network resources based on the identified best-fit network-based parsimony tree, for providing the requested network services.

Novel tools and techniques are provided for implementing intent-based orchestration using network parsimony trees. In various embodiments, in response to receiving a request for network services that comprises desired characteristics and performance parameters for the requested network services without information regarding specific hardware, hardware type, location, or network, a computing system might generate a request-based parsimony tree based on the desired characteristics and performance parameters. The computing system might access, from a datastore, a plurality of network-based parsimony trees that are each generated based on measured network metrics, might compare the request-based parsimony tree with each of one or more network-based parsimony trees to determine a fitness score for each network-based parsimony tree, and might identify a best-fit network-based parsimony tree based on the fitness scores. The computing system might identify and might allocate network resources based on the identified best-fit network-based parsimony tree, for providing the requested network services.

The technologies described herein are generally directed to facilitating the allocation, scheduling, and management of network slice resources. According some embodiments, a system can comprise a processor and a memory that can store executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include identifying a slice configuration of a network slice that was allocated to a user device, the slice configuration being based on a characteristic of the user device, wherein a capacity of a resource of a network device of a network was previously assigned to support the network slice based on the slice configuration. The operations can further include monitoring usage of the network slice by the user device during the usage of the network slice, resulting in monitored usage of the network slice. Further, based on and during the monitored usage of the network slice, operations can include facilitating modifying the capacity of the resource assigned to support the network slice.

Providing users with smooth and reliable applications in a cloud based setting is a desirable goal. An approach to pre-allocating cloud computing resources may be provided to improve user experience. A user device may monitor an environment for individual user behaviors with visual and/or audio sensors. Based on data from the visual and/or audio sensors individual behaviors may be identified. Individual behaviors may be identified and associated with a cloud computing resource request. Computing resources in the cloud may be reserved or pre-allocated based on the cloud computing resource request. The pre-allocated computing resources can improve user experience through reduced wait time and improve initial cloud-based application response.