A request may be identified having one or more constraints for accessing disaggregated resources in a computing environment. One or more resources in a plurality of disaggregated resources may be identified based on the request. Computing server instances may be dynamically orchestrated using the one or more resources in the plurality of disaggregated resources based on the one or more constraints.

Route exchange in a plurality of network controller appliances on a per-tenant basis is disclosed. In one aspect, a method includes receiving, from a network management system and at a first network controller appliance, a designation of at least two tenants to be hosted on the first network controller appliance, the first network controller appliance being one of a plurality of network controller appliances in a SD-WAN; sending, from the first network controller appliance to other network controller appliances of the plurality of network controller appliances, a tenant list query message to obtain a corresponding tenant list of each of the other network controller appliances; and receiving a corresponding response from each of the other network controller appliances indicating the corresponding tenant list of each of the other network controller appliances, the corresponding response being used to update the tenant list on the first network controller appliance.

Technologies for dynamic accelerator selection include a compute sled. The compute sled includes a network interface controller to communicate with a remote accelerator of an accelerator sled over a network, where the network interface controller includes a local accelerator and a compute engine. The compute engine is to obtain network telemetry data indicative of a level of bandwidth saturation of the network. The compute engine is also to determine whether to accelerate a function managed by the compute sled. The compute engine is further to determine, in response to a determination to accelerate the function, whether to offload the function to the remote accelerator of the accelerator sled based on the telemetry data. Also the compute engine is to assign, in response a determination not to offload the function to the remote accelerator, the function to the local accelerator of the network interface controller.

Methods and systems are provided for performing lossy dropping and ECN marking in a flow-based network. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow are acknowledged after reaching the egress point of the network, and the acknowledgement packets are sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform per-flow packet dropping and ECN marking.

In general, various aspects of the techniques are described in this disclosure for distributed label assignment for labeled routes. In one example, a method includes obtaining, by a first thread of a plurality of execution threads for at least one routing protocol process executing on processing circuitry of a network device, an allocation of first labels drawn from a label space for a network service; adding, by the first thread, the first labels to a first local label pool for the first thread; generating, by the first thread, after obtaining the allocation of the first labels, a labeled route comprising a route for the network service and a label assigned by the first thread from the first local label pool; and outputting, by the network device, the labeled route.

“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.

Various techniques for dynamic path selection and data flow forwarding are disclosed. For example, various systems, processes, and computer program products for dynamic path selection and data flow forwarding are disclosed for providing dynamic path selection and data flow forwarding that can facilitate preserving/enforcing symmetry in data flows as disclosed with respect to various embodiments.

Systems and methods to support flexible scaling and dynamic resource allocation for virtualized cable data plane applications. The system includes a head end together with a node to provide data to customer devices. A container operating that includes a data plane application that provides packets of data for transmission to the node. The data plane application is instantiated with at least one of a virtual networking function and a computing resource function.

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.