Abstract: In one embodiment, an apparatus includes a multi-socket motherboard, a processor connected to a first socket on the multi-socket motherboard, and an RDMA (Remote Direct Memory Access) interface module connected to a second socket on the multi-socket motherboard and in communication with the processor over a coherency interface. The RDMA interface module provides an inter-server interface between servers in an RDMA domain. A method for transferring data between servers with RDMA interface modules is also disclosed herein.

Abstract: Embodiments herein describe association rules (e.g., affinity and anti-affinity rules) that a wireless device can use to optimize its performance in a Wi-Fi network. While BSS coloring is typically used to eliminate color collisions, the embodiments herein use BSS coloring to define what BSS colors should be on the same channel and which should not. For example, an affinity rule can indicate that a wireless device assigned a first BSS color (e.g., red) can share the same channel with wireless devices (or BSSs) assigned a second BSS color (e.g., green). In contrast, an anti-affinity rule can indicate that a wireless device in the red BSS color cannot share a channel with a wireless device assigned to a third BSS color (e.g., blue). The embodiments herein permit the wireless devices to be grouped with, or separated from, wireless devices having different BSS colors.

Abstract: A method includes obtaining performance characterization values from endpoints managed by a first fog node at a first hierarchical level in a hierarchy of fog nodes. The method includes changing a first operating characteristic of the wireless network based on the performance characterization values. The first operating characteristic affects the operation of one or more of the endpoints. The method includes transmitting a portion of the performance characterization values to a second fog node at a second hierarchical level in the hierarchy of fog nodes. The method includes changing a second operating characteristic of the wireless network based on an instruction from the second fog node. The second operating characteristic affects the operation of the first fog node and/or other fog nodes at the first hierarchical level. Changing one or more of the first operating characteristic and the second operating characteristic satisfies an operating threshold for the wireless network.

Abstract: Techniques for establishing network quality of service for an internet of things device are described. A manufacturer usage description identifier relating to the internet of things device is received. The internet of things device is coupled to a communication network. Quality of service parameters relating to the internet of things device and the communication network are determined based on the manufacturer usage description identifier. The quality of service parameters are provided to a network policy controller. The network policy controller is configured to establish a quality of service for the internet of things device on the communication network based on the one or more quality of service parameters.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Meta behavioral analytics techniques include, at one or more network devices that are operatively coupled to a plurality of behavioral analytics systems associated with a network or system, monitoring data outputs of the plurality of behavioral analytics systems that are representative of activity in the network or system. The one or more network devices correlate the data outputs from two or more of the plurality of behavioral analytics systems that are dedicated to analyzing different subject matter domains. Additionally, based on the correlating, the one or more network devices detect a previously unidentified condition in (a) the network or system; or (b) one of the plurality of behavioral analytics systems.

Abstract: A method includes obtaining performance characterization values from endpoints managed by a first fog node at a first hierarchical level in a hierarchy of fog nodes. The method includes changing a first operating characteristic of the wireless network based on the performance characterization values. The first operating characteristic affects the operation of one or more of the endpoints. The method includes transmitting a portion of the performance characterization values to a second fog node at a second hierarchical level in the hierarchy of fog nodes. The method includes changing a second operating characteristic of the wireless network based on an instruction from the second fog node. The second operating characteristic affects the operation of the first fog node and/or other fog nodes at the first hierarchical level. Changing one or more of the first operating characteristic and the second operating characteristic satisfies an operating threshold for the wireless network.

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: In one embodiment, an Internet of Things (IoT) device in a network establishes connections with a plurality of peers. The device identifies an event involving the IoT device. The device generates a GOAWAY message that includes metadata regarding the event within a metadata field of the message. The GOAWAY message indicates that the IoT device is not accepting new connections. The device sends the GOAWAY message to one or more of the peers.

Abstract: In one embodiment, a device including a processor, and a memory to store data used by the processor, wherein the processor is operative to run a manufacturer usage description (MUD) controller operative to obtain a MUD profile of an Internet of Things (IoT) device from a MUD server, the MUD profile of the IoT device including: access rights of the IoT device, and any one or more of the following a default device username and/or a default device password of the IoT device, a recommended/required device password complexity of the IoT device, at least one service that should be enabled/disabled on the IoT device, and/or allowed security protocols and/or ciphers for communication to and/or from the IoT device, enforce security of the IoT device according to the MUD profile of the IoT device. Related apparatus and methods are also described.

Abstract: In one embodiment, a method comprises detecting, by an apparatus, establishment of a stateful application session between a mobile endpoint device and a stateful virtualized application executed by a first virtualization host in a data network, the mobile endpoint device establishing a network connection with the stateful virtualized application via a first wireless connection with a first network access point; generating, by the apparatus, a connection container comprising a connection identifier uniquely identifying the network connection, connection metadata describing the network connection, and application state metadata describing execution of the stateful virtualized application for the mobile endpoint device; and outputting, by the apparatus, the application state metadata for continuous execution of the stateful virtualized application by a second virtualization host associated with a second network access point, based on determining the mobile endpoint device connecting with the second network acces

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.

Abstract: In one embodiment, a system includes a central hub comprising a power source, a data switch, a coolant system, and a management module, a plurality of network devices located within an interconnect domain of the central hub, and at least one combined cable connecting the central hub to the network devices and comprising a power conductor, a data link, a coolant tube, and a management communications link contained within an outer cable jacket.

Abstract: In one embodiment, a device including a processor, and a memory to store data used by the processor, wherein the processor is operative to run a manufacturer usage description (MUD) controller operative to obtain a MUD profile of an Internet of Things (IoT) device from a MUD server, the MUD profile of the IoT device including: access rights of the IoT device, and any one or more of the following a default device username and/or a default device password of the IoT device, a recommended/required device password complexity of the IoT device, at least one service that should be enabled/disabled on the IoT device, and/or allowed security protocols and/or ciphers for communication to and/or from the IoT device, enforce security of the IoT device according to the MUD profile of the IoT device. Related apparatus and methods are also described.

Abstract: In one embodiment, a classification device in a computer network analyzes data from a given device in the computer network, and classifies the given device as a particular type of device based on the data. The classification device may then determine whether a manufacturer usage description (MUD) policy exists for the particular type of device. In response to there being no existing MUD policy for the particular type of device, the classification device may then determine patterns of the analyzed data, classify the patterns into context-based policies, and generate a derived MUD policy for the particular type of device based on the context-based policies. The classification device may then apply one of either the existing or derived MUD policy for the given device within the computer network.

Abstract: In one embodiment, an Internet of Things (IoT) device in a network establishes connections with a plurality of peers. The device identifies an event involving the IoT device. The device generates a GOAWAY message that includes metadata regarding the event within a metadata field of the message. The GOAWAY message indicates that the IoT device is not accepting new connections. The device sends the GOAWAY message to one or more of the peers.

Abstract: In one embodiment, a system includes a central hub comprising a power source, a data switch, a coolant system, and a management module, a plurality of network devices located within an interconnect domain of the central hub, and at least one combined cable connecting the central hub to the network devices and comprising a power conductor, a data link, a coolant tube, and a management communications link contained within an outer cable jacket.

Abstract: In one embodiment, a method comprises detecting, by an apparatus, establishment of a stateful application session between a mobile endpoint device and a stateful virtualized application executed by a first virtualization host in a data network, the mobile endpoint device establishing a network connection with the stateful virtualized application via a first wireless connection with a first network access point; generating, by the apparatus, a connection container comprising a connection identifier uniquely identifying the network connection, connection metadata describing the network connection, and application state metadata describing execution of the stateful virtualized application for the mobile endpoint device; and outputting, by the apparatus, the application state metadata for continuous execution of the stateful virtualized application by a second virtualization host associated with a second network access point, based on determining the mobile endpoint device connecting with the second network acces