Abstract: In one embodiment, a method comprises: receiving, by a process, an executed function flow of a daisy chained serverless function-as-a-service (FaaS) function, the executed function flow having been injected with a particular trace identifier in response to an initial event trigger and span identifiers having been injected by each service that was executed; generating, by the process, a serverless flow graph associated with the particular trace identifier based on linking a path of serverless functions according to correlation of the span identifiers between the serverless functions; performing, by the process, a trace-based analysis of the serverless flow graph through comparison to a baseline of expectation; detecting, by the process, one or more anomalies in the serverless flow graph according to the trace-based analysis; and mitigating, by the process, the one or more anomalies in the serverless flow graph.

Abstract: This disclosure describes using a dynamic proxy for securing communications between a source within a cloud environment and an application container. The techniques include intercepting traffic directed to an application container, analyzing the traffic and traffic patterns, and allowing or preventing the traffic from being delivered to the application container based on the analysis. A traffic analysis engine may determine whether the traffic is considered safe and is to be allowed to be delivered to the application container, or whether the traffic is considered unsafe and is to be prevented from being delivered to the application container, According to some configurations, the address(es) to the network interfaces (e.g., WIFI or Eth0) are abstracted to help ensure security of the application containers.

Abstract: In one embodiment, a deep fusion reasoning engine receives network telemetry data collected from a network. The deep fusion reasoning engine learns resource utilizations for different heuristic packages that can be used in the network to evaluate operation of the network. The deep fusion reasoning engine selects one of the heuristic packages based on the resource utilizations learned for the different heuristic packages. The selected heuristic package comprises a subservice and a set of rules to be evaluated. The deep fusion reasoning engine deploys the selected heuristic package for execution by a device in the network to evaluate operation of the network using the set of rules.

Abstract: Methods are provided for predictive policy enforcement using encapsulated metadata. The methods involve obtaining a packet of an encapsulated traffic flow that is transported in a software-defined wide area network (SD-WAN) or in a cloud network. The packet includes a network virtualization tunneling header with an appended service plane protocol header and a payload. The methods further involve extracting, from the appended service plane protocol header, without performing deep packet inspection, enriched metadata that includes fields for one or more attributes related to a source of the packet or a destination of the packet, determining at least one network policy based on the enriched metadata, and applying, to the packet, the at least one network policy that relates to gathering analytics and/or transporting the encapsulated traffic flow to the destination.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for configurable aprons for expanded binning. Aspects of the present disclosure include identifying one or more pixel tiles in at least one bin and determining edge information for each pixel tile of the one or more pixel tiles. The edge information may be associated with one or more pixels adjacent to each pixel tile. The present disclosure further describes determining whether at least one adjacent bin is visible based on the edge information for each pixel tile, where the at least one adjacent bin may be adjacent to the at least one bin.

Abstract: Failure prediction signaling and cognitive user migration may be provided. A client device may receive at least a portion of failure prediction data. The client device may then analyze the at least the portion of the failure prediction data. The client device may then roam from a first computing device to a second computing device in response to analyzing the at least the portion of the failure prediction data.

Abstract: In one illustrative example, a user plane (UP) entity for use in a mobile network may receive a data packet from a user equipment (UE) operative to communicate in one or more sessions via a serving base station (BS) (e.g. eNB or gNB) of the mobile network. The UP entity may detect, in a header (e.g. SRH) of the data packet, an identifier indicating a new serving BS or session of the UE. The identifier may be UE- or BS-added data (e.g. iOAM data) that is inserted in the header by the UE or BS. In response, the UP entity may cause a message to be sent to an analytics function (e.g. a NWDAF) to perform analytics for session or flow migration for the UE.

Abstract: Techniques for identifying nodes in a data center fabric that are affected by a failure in the fabric, and selectively sending disaggregation advertisements to the nodes affected by the failure. The techniques include a process where a component monitors the network fabric to identify communication paths between leaf nodes, and determines what leaf nodes would be affected by a failure in those communication paths. The component may detect a failure in the network and determine which communication paths, and thus which leaf nodes, are affected by the failure and send disaggregation advertisements to the affected leaf nodes. In some examples, ingress leaf nodes send data through the fabric that indicate egress nodes for the communication paths. Intermediate nodes along may receive the data from the leaf nodes to identify communication paths, and the notify only affected nodes upon detecting a failure in the network.

Abstract: Techniques for utilizing edge nodes disposed throughout a multi-site cloud computing network to generate a probe packet including indicators that guarantee the use of forward and return route paths to accurately measure the network performance of a route path between two endpoints in a wide area network (WAN). An edge node disposed in a site of the multi-site cloud computing network may store in virtual memory associated with the edge node, a mapping between route paths, usable to send data from the edge node to remote edge nodes in remote sites, and route indicators. A probe packet may include a data portion for measuring the network performance of a route path, a portion including local and remote discriminators, and/or an inner and an outer header.

Abstract: A method comprising: at a management entity configured to communicate with a network: upon detecting a performance problem on a network path in the network, generating a trigger probe having a correlation identifier, the trigger probe configured to transit the network path and, on one or more designated network nodes of the network path, trigger (i) capturing a full device state, including a control plane state and a data plane state, and (ii) exporting a report of the full device state with the correlation identifier; sending the trigger probe along the network path; receiving, from each of the one or more designated network nodes, the report that includes the correlation identifier and the full device state; and correlating each report to the performance problem based on the correlation identifier in each report, to diagnose a root cause of the performance problem using the full device state in each report.

Abstract: This disclosure describes techniques for performing application-based tagging. An example method is performed by a virtual socket of a device. The method includes receiving non-packetized data from an application, generating a label based on the application, and providing the non-packetized data and the label to a kernel of the device.

Abstract: In one embodiment, a service chain data packet is instrumented as it is communicated among network nodes in a network providing service-level and/or networking operations visibility. The service chain data packet includes a particular header identifying a service group defining one or more service functions, and is a data packet and not a probe packet. A network node adds networking and/or service-layer operations data to the particular service chain data packet, such as, but not limited to, in the particular header. Such networking operations data includes a performance metric or attribute related to the transport of the particular service chain packet in the network. Such service-layer operations data includes a performance metric or attribute related to the service-level processing of the particular service chain data packet in the network.

Abstract: The present disclosure includes methods, systems, and non-transitory computer-readable media for validating data in a data structure used for forwarding packets by a network device comprising sending a data packet probe identifying a destination and including a segment ID, wherein the segment ID maps to a first interpretation by a receiving router to perform an action on the data packet probe to rewrite a portion of a destination address in a header of the data packet probe, and to redirect the data packet probe to the network device that initiated the data packet probe.

Abstract: In one embodiment, a method herein comprises: establishing, by a process, a virtual private network connection (VPN connection) with a particular VPN gateway; requesting, by the process, observability monitoring through the particular VPN gateway, wherein requesting results in a controller being informed about the particular VPN gateway and a domain of the particular VPN gateway; receiving, by the process, test specifics from the controller based on the particular VPN gateway and the domain of the particular VPN gateway; and executing, by the process, one or more tests to the particular VPN gateway based on the test specifics.

Abstract: Techniques for identifying nodes in a data center fabric that are affected by a failure in the fabric, and selectively sending disaggregation advertisements to the nodes affected by the failure. The techniques include a process where a component monitors the network fabric to identify communication paths between leaf nodes, and determines what leaf nodes would be affected by a failure in those communication paths. The component may detect a failure in the network and determine which communication paths, and thus which leaf nodes, are affected by the failure and send disaggregation advertisements to the affected leaf nodes. In some examples, ingress leaf nodes send data through the fabric that indicate egress nodes for the communication paths. Intermediate nodes along may receive the data from the leaf nodes to identify communication paths, and the notify only affected nodes upon detecting a failure in the network.

Abstract: Techniques and apparatus for determining quality of experience (QoE) for wireless communications are described. One technique involves transmitting a QoE support message to an access point (AP) within an access network. The QoE support message queries whether the AP supports providing key performance indicators (KPI(s)) indicative of QoE provided by the access network. An indication of whether the AP supports providing the KPI(s) is received in response to the QoE support message. The KPI(s) are received when the AP supports providing the KPI(s). A determination is made whether to communicate with the AP based at least in part on the KPI(s). Communications are then performed in accordance with the determination.

Abstract: In one embodiment, an access policy enforcement service receives a user authentication request from an end-user device. The access policy enforcement service identifies a telemetry collection intent from the user authentication request. The access policy enforcement service determines a monitoring policy based on the telemetry collection intent identified from the user authentication request. The access policy enforcement service configures, according to the monitoring policy, one or more telemetry collection agents to collect telemetry for traffic associated with the end-user device.

Abstract: Techniques for policy-based failure handling of data that is received for processing by failed edge services are described herein. The techniques may include receiving, at an edge node of a network, a data handling policy for a service hosted on the edge node. The service may be configured to process traffic on behalf of an application hosted by a cloud-based platform. In some examples, the data handling policy may be stored in a memory that is accessible to the edge node. The techniques may also include receiving traffic at the edge node that is to be processed at least partially by the service. At least partially responsive to detecting an error associated with the service, the edge node may cause the traffic to be handled according to the data handling policy while the service is experiencing the error.

Abstract: A method, computer system, and computer program product are provided for controlling data access and visibility using a context-based security policy. A request from an endpoint device to receive data is received at a server, wherein the request includes one or more contextual attributes of the endpoint device including an identity of a user of the endpoint device. The one or more contextual attributes are processed to determine that the endpoint device is authorized to receive the data. A security policy is determined for the data based on the one or more contextual attributes. The data is transmitted, including the security policy, to the endpoint device, wherein the endpoint devices enforces the security policy to selectively permit access to the data by preventing the endpoint device from displaying the data to an unauthorized individual.

Abstract: A method, computer system, and computer program product are provided for decentralized machine learning. A plurality of computing networks are identified by determining that each computing network of the plurality of computing networks satisfies a predetermined number of criteria. A decentralized learning agent is provided to each computing network, wherein the decentralized learning agent is provided with input parameters for training and is trained using training data associated with a computing network to which the decentralized learning agent is provided. A plurality of learned parameters are obtained from the plurality of computing networks, wherein each learned parameter of the plurality of learned parameters is obtained by training the decentralized learning agent provided to each respective computing network. A global model is generated based on the plurality of learned parameters.