Abstract: In one embodiment, a first device in a network receives information regarding one or more nodes in the network. The first device determines a property of the one or more nodes based on the received information. The first device determines a degree of trustworthiness of the one or more nodes based on the received information. The first device attests to the determined property and degree of trustworthiness of the one or more nodes to a verification device. The verification device is configured to verify the attested property and degree of trustworthiness.

Abstract: In one embodiment, a device receives data regarding a plurality of heterogeneous computing environments. The received data comprises measured application metrics for applications executed in the computing environments and indications of processing capabilities of the computing environments. The device generates a training dataset by applying a machine learning-based classifier to the received data regarding the plurality of existing heterogeneous environments. The device trains a machine learning-based configuration engine using the training dataset. The device uses the configuration engine to generate configuration parameters for a particular heterogeneous computing environment based on one or more system requirements of the particular heterogeneous computing environment. The device provides the configuration parameters to the particular heterogeneous computing environment.

Abstract: A network device may connect to a smart-enabled network. Once connected, the network device may receive a network address for a network management server (NMS). Having the network address for the NMS, the network device may generate a vCard comprising the attributes necessary for registering with the NMS. The network device may then communicate the vCard to the NMS. The NMS may then be configured to identify, register, and add the network device to a directory.

Abstract: Embodiments herein describe a perch for screening drones before permitting access to a restricted geographic region. The perch may include various scanners for evaluating the payload of the drone, its hardware, and flight control software. In one embodiment, the screening perch includes a conveyor belt that moves the drone through various scanners or stages in the perch. In one embodiment, the perch ensures the drone is properly configured to enter the restricted geographic region. The region may include multiple requirements or criteria that must be satisfied before a drone is permitted to enter. For example, the drone may need a signed flight plan, cargo that is less than a certain percentage of its weight, or an approved flight controller before being permitted into the restricted region. In this manner, the perch serves as a controlled entrance point for drones attempting to enter the restricted region.

Abstract: In one embodiment, a server determines a particular computer network outside of a lab environment to recreate, and also determines, for the particular computer network, hardware components and their interconnectivity, as well as installed software components and their configuration. The server then controls interconnection of lab hardware components within the lab environment according to the interconnectivity of the hardware components of the particular computer network. The server also installs and configures lab software components on the lab hardware components according to the configuration of the particular computer network. Accordingly, the server operates the installed lab software components on the interconnected lab hardware components within the lab environment to recreate operation of the particular computer network within the lab environment, and provides information about the recreated operation of the particular computer network.

Abstract: In one embodiment, a first device in a network receives information regarding one or more nodes in the network. The first device determines a property of the one or more nodes based on the received information. The first device determines a degree of trustworthiness of the one or more nodes based on the received information. The first device attests to the determined property and degree of trustworthiness of the one or more nodes to a verification device. The verification device is configured to verify the attested property and degree of trustworthiness.

Abstract: In one embodiment, a ceiling tile, configured to be positioned above a given area, comprises a plurality of sensors and a plurality actuators embedded within the ceiling tile, each sensor configured to sense a corresponding feature of the area, and each actuator configured to modify a corresponding feature of the area. The plurality of sensors and plurality of actuators are configured to interact with a controlling device that controls a plurality of ceiling tiles for the area. In another embodiment, one or more floor tiles with one or more sensors (e.g., and actuators) may also be located within the area, and the controlling device further controls the floor tiles, accordingly.

Abstract: In one embodiment, a supervisory device in a network maintains a plurality of node profiles for nodes in the network. The supervisory device receives, from a fog computing device in the network, node data associated with a particular node in the network. The supervisory device determines a node profile for the particular node based on the received node data and the maintained plurality of node profiles. The supervisory device causes installation of a fog computing application to the fog computing device based on the determined node profile for the particular node. The fog computing application is configured to process the node data associated with the particular node.

Abstract: Disclosed is a router (and method) for virtualizing a control plane of the router without redundancy. The router can include a processor, a data plane, a control plane, and a computer-readable storage medium having stored therein instructions which, when executed by the processor, cause the processor to request, a cloud service to instantiate a virtual instance of the control plane, receive a confirmation of instantiation of the virtual instance, transfer to the virtual instance of the control plane, an active state of the control plane, perform offline services (e.g., configuration change, operating system update, or firmware upgrade, etc.) and in response to completion of the offline services, receive the active state.

Abstract: In one embodiment, an intermediate node, of a multi-stage process path through a computer network, receives a workload message with an associated latency budget to complete the multi-stage process at a final stage device. In response, the intermediate node determines a current latency from an initial stage device for the workload message to the receiving of the workload message, and also determines a remaining portion of the latency budget based on the current latency. In response to the remaining portion of the latency budget being less than expected at the intermediate node, the intermediate node may perform one or more latency-reducing actions, and then transmits the workload message toward the final stage device.

Abstract: In one embodiment, a device in a network receives a set of sensor data from a plurality of sensors deployed in a location. The device determines a physical layout for furnishings in the location based on the received set of sensor data. One or more of the furnishings is equipped with one or more actuators configured to move the equipped furnishing in one or more directions. The device generates an instruction for the one or more actuators of a particular one of the furnishings based on the determined physical layout for the furnishings. The device sends the instruction to the one or more actuators of the particular furnishing, to implement the determined physical layout.

Abstract: In one embodiment, an autonomous vehicle receives a location of a fiber optic cable repeater of a fiber optic cable. The autonomous vehicle navigates the vehicle to the location of the fiber optic cable repeater and interfaces an optical time domain reflectometer (OTDR) of the autonomous vehicle with an OTDR port of the fiber optic cable repeater. The autonomous vehicle performs OTDR measuring of the fiber optic cable via the OTDR port of the fiber optic cable repeater, and sends a result of the OTDR measuring of the fiber optic cable to a supervisory device.

Abstract: Various implementations disclosed herein enable transforming mutable wireless coverage areas using network coverage vehicles (NVCs) that are orchestrated by a network coverage controller. In various implementations, the method includes receiving coverage area performance characterization values from NCVs configured to provide a plurality of mutable wireless coverage areas. In various implementations, an arrangement of the mutable wireless coverage areas mutably defines the service area, which changes in accordance with changes to the arrangement of the mutable wireless coverage areas. In various implementations, the method also includes determining NCV operation adjustments for some of the NCVs based on the received coverage area performance characterization values in accordance with a service performance metric; and, altering an arrangement of one or more of the plurality of mutable wireless coverage areas within the service area by providing the NCV operation adjustments to some of the NCVs.

Abstract: In one embodiment, a device in a network joins a fog-based malware defense cluster comprising one or more peer devices. The device and each peer device in the cluster are configured to execute a different set of local malware scanners. The device receives a file flagged as suspicious by a node in the network associated with the device. The device determines whether the local malware scanners of the device are able to scan the file. The device sends an assessment request to one or more of the peer devices in the malware defense cluster, in response to determining that the local malware scanners of the device are unable to scan the file.

Abstract: In one embodiment, a device receives data regarding a plurality of heterogeneous computing environments. The received data comprises measured application metrics for applications executed in the computing environments and indications of processing capabilities of the computing environments. The device generates a training dataset by applying a machine learning-based classifier to the received data regarding the plurality of existing heterogeneous environments. The device trains a machine learning-based configuration engine using the training dataset. The device uses the configuration engine to generate configuration parameters for a particular heterogeneous computing environment based on one or more system requirements of the particular heterogeneous computing environment. The device provides the configuration parameters to the particular heterogeneous computing environment.

Abstract: Presented herein are service-function chaining techniques that enable data plane signaling of a packet as a candidate for capture at various network nodes along a service function path of a service function chain. That is, a capture signal is embedded within the respective packet that carries a user traffic. The signaling occurs in-band, via the data plane, such that classification of the packet for capture beneficially occurs, at the ingress node of the network, once to which subsequent network nodes along a service function path are signaled to capture or further inspect the packet for capture. Service function chaining treats service functions as resources with associated attributes available for scheduled consumption to which selective traffic are steered according to a policy construct to the requisite network-service resources.

Abstract: Systems, methods, and computer-readable media for orchestrating cloud to fog interactions. In some examples, a method can involve partitioning an application into software containers, each of the software containers being configured to host a respective component of the application. The method can further involve identifying nodes on respective hierarchical layers of a hierarchical cloud-fog architecture for hosting the software containers on the respective hierarchical layers of the cloud-fog architecture. The hierarchical cloud-fog architecture can include one or more cloud layers and one or more fog layers. The method can also involve deploying the software containers at the nodes on the respective hierarchical layers of the cloud-fog architecture.

Abstract: In one embodiment, a first wireless unmanned aerial vehicle (UAV)-locating signal is transmitted by a wireless network access point in a network based on a first UAV-locating mode selected from a plurality of UAV-locating modes. The wireless network access point receives a wireless signal in response to the first transmitted UAV-locating signal, the wireless signal indicative of a location of an airborne UAV, and causes the determination of the location of the airborne UAV based on the received wireless signal. The wireless network access point transmits a second wireless UAV-locating signal based on a second UAV-locating mode selected from the plurality of UAV-locating modes. The selected UAV-locating modes control an emission pattern of an antenna of the wireless network access point.

Abstract: Disclosed are systems, methods and non-transitory computer-readable mediums for dynamically presenting and updating a directed time graph displayed in a graphical user interface. In some examples, the method can include displaying a suggested path within a graphical user interface on a computer screen, the suggested path can include outstanding issues of elements of a network. The displaying the suggested path can include determining based on one or more factors an efficient ordering of the outstanding issues and ordering the outstanding issues based on the one or more factors. The method can also include monitoring, at regular intervals, updates to the one or more outstanding issues and automatically updating the suggested path, by a processor, based on the updates to the one or more outstanding issues.

Abstract: In one embodiment, a method includes receiving flight path data regarding the presence of an unmanned aerial vehicle (UAV) at a location at a future time, detecting the presence of the UAV at the location at the future time, determining radio identity data of the UAV using a radio mode of identification, determining optical identity data of the UAV using an optical mode of identification, and certifying the UAV based on a comparison of the radio identity data and the optical identity data to the flight path data.