Abstract: An architecture for providing an on-demand dynamic cloudlet instantiation by leveraging a grouping of software defined network devices and their respective associated analytics modules. A method can comprise receiving quality of service data representing a 5G small cell device of a group of 5G small cell devices; generating a policy rule representing a predictive policy to facilitate an instantiation of a dynamic on-demand cloudlet node into a fog of dynamic on-demand cloudlet node instantiations; and facilitating the instantiation of the dynamic on-demand cloudlet node into the fog, by a hybrid fiber coaxial device, based on the policy rule and an indication received from the hybrid fiber coaxial that a traffic surge in communications with the 5G small cell device has occurred.

Abstract: Avatars may be displayed in a multiuser communication session using various spatial modes. One technique for presenting avatars includes presenting avatars such that an attention direction of the avatar is retargeted to match the intent of the remote user corresponding to the avatar. Another technique for presenting avatars includes a pinned mode in which a spatial relationship between one or more avatars remains displayed in a consistent spatial relationship to a local user regardless of movements of the local user. Another technique for presenting avatars includes providing user-selectable presentation modes between a room scale mode and a stationary mode for presenting a representation of a multiuser communication session.

Abstract: A method and system for performing at least one service are disclosed. The method and system include receiving a communication for a data source at a wrapper. The wrapper includes a dispatcher and at least one service. The dispatcher receives the communication and is data agnostic. The method and system also include providing the communication from the dispatcher to the data source and to the at least one service. The at least one service inspects the communication and may perform additional functions.

Abstract: The subject matter of this specification can be implemented in, among other things, a method and a system to perform the method that includes receiving a request from a client device to execute an application, selecting servers that provide remote desktop environment and host the requested application, determining, based on a priority level for a client session to be established, a capacity of system resources and a current utilization level of each server, that the client device is to be directed to a first server, the first server having an expected utilization level that satisfies a threshold condition, and directing the request to the first server to establish the client session and to execute the requested application as part of the client session.

Abstract: A device may receive, from a network device in near-real time, a packet of data associated with network traffic of a network, wherein the packet includes privacy-related data and network-related data. The device may read the privacy-related data from the packet. The device may generate anonymous data based on the privacy-related data, wherein the anonymous data obscures the privacy-related data. The device may generate a mapping between the anonymous data and the privacy-related data. The device may combine the anonymous data and the network-related data to generate a masked packet. The device may provide the masked packet to a server device. The device may receive, from the server device, data identifying a recommendation that is generated by processing the masked packet with an artificial intelligence model. The device may perform one or more actions based on the recommendation.

Abstract: Systems and methods are provided for an application platform with flexible permissioning.

Abstract: Techniques for machine learning inferencing endpoint discovery in a distributed computing system are discloses herein. In one example, a method includes searching a database containing machine learning endpoint records having data representing values of execution latency or prediction accuracy corresponding inferencing endpoints deployed in the distributed computing system. The method also includes generating a list of inferencing endpoints matching the individual target values and determining whether a count of the inferencing endpoints in the generated list exceeds a preset threshold. In response to determining that the identified count does not exceed the preset threshold, the method includes instantiating one or more additional inferencing endpoints in the distributed computing system based on the individual target values in the received query.

Abstract: According to one or more embodiments of the disclosure, an example process herein may comprise: receiving configuration of an extension to manage one or more particular endpoints and data collectors for a particular tenant of an extensibility platform; determining a specification of a container containing one or more particular functions configured to perform required data transformations for the extension; providing one or more shared egress assistant functions configured to receive data from the one or more particular functions; and sending the data from the one or more shared egress assistant functions onto a common ingest for further processing, wherein the one or more particular functions and the one or more shared egress assistant functions are executed as functions-as-a-service at runtime.

Abstract: An orchestration engine that interfaces with the various clouds to implement the system solution. The orchestration engine builds, configures, and converges the single system solution. The orchestration engine builds the system by instantiating machines and services on the various clouds. The orchestration engine configures the system according to input from the client and the solution provider and then converges the various systems by establishing relationships and connections between the various services. Optionally, the orchestration engine also runs tests to verify that the system is working properly. Once the system has been built, configured and converged, the orchestration engine provides the client with a single entry point for using the system solution.

Abstract: Apparatus, methods, and systems that operate to provide interactive streaming content identification and processing are disclosed. An example apparatus includes a classifier to determine an audio characteristic value representative of an audio characteristic in audio; a transition detector to detect a transition between a first category and a second category by comparing the audio characteristic value to a threshold value among a set of threshold values, the set of threshold values corresponding to the first category and the second category; and a context manager to control a device to switch from a first fingerprinting algorithm to a second fingerprinting algorithm different than the first fingerprinting algorithm, responsive to the detected transition between the first category and the second category.

Abstract: A streaming platform reader includes: a plurality of reader threads configured to retrieve messages from a plurality of partitions of a streaming platform, wherein each message in the plurality of partitions is associated with a unique identifier; a plurality of queues coupled to the plurality of reader threads configured to store messages or an end of partition signal from the reader threads, wherein each queue includes a first position that stores the earliest message stored by a queue; a writer thread controlled by gate control logic that: compares the identifiers of all of the messages in the first positions of the queues of the plurality of queues, and forwards, to a memory, the message associated with the earliest identifier; and wherein the gate control logic blocks the writer thread unless each of the queues contains a message or an end of partition signal.

Abstract: A method and system for using a trained machine learning model with respect to information pertaining to a job title of multiple job titles to determine a job family of multiple job families that corresponds to the job title is disclosed. First input comprising information identifying the job title associated with an organization of a plurality of organizations is provided to the trained machine learning model. One or more outputs identifying (i) an indication of the job family that identifies a category of personnel positions that are categorized based on one or more characteristics that are shared between the personnel positions of the category, and (ii) a level of confidence that the job family corresponds to the job title is obtained from the trained machine learning model.

Abstract: A discovery application on a computing system is provided. The discovery application executes a discovery pattern comprising a sequence of operations for discovery of resources within a load balancing system, wherein execution of the discovery pattern corresponds to making one or more application programming interface (API) calls to an API associated with a network address of the load balancing system; receives a response to the one or more API calls from the load balancing system, wherein the response comprises a payload of information associated with the resources; and updates, based on the response and in a configuration management database (CMDB), one or more configuration items (CIs) associated with the resources.

Abstract: The present disclosure generally relates to controlling access to resources by selectively processing requests stored in a task queue to prioritize certain requests over others, thereby preventing automated scripts from accessing the resources. More specifically, the present disclosure relates to a normalization and prioritization system for controlling access to resources by queuing resource requests based on a client-defined normalization process that uses one or more data sources.

Abstract: The present disclosure generally provides a system and method for providing a real-time digital virtual audience. The system and method may include processing video associated with an event to produce a corresponding data stream, combining the corresponding data stream with at least one sequence of predictability data stream to create a digital framework, processing the digital framework into at least one representation of at least one object depicted in the video, and transmitting the at least one representation to one or more communication devices associated with one or more virtual audience members, wherein the one or more virtual audience members are viewing the same event using their respective communication devices.

Abstract: An aspect of the present disclosure discloses a method for diagnosing a network performance in an apparatus for diagnosing network performance connected to a switching device provided between a first entity and a second entity. The method may include: obtaining at least one mirrored packet for at least one packet transmitted between the first entity and the second entity based on mirroring from the switching device, calculating performance related indicator representing performance of a network service associated with the first entity and the second entity based on at least a part of information included in the at least one mirrored packet, and storing the calculated performance related indicators.

Abstract: What is disclosed is tagging a first flow of a multi-tenant virtual private network (VPN) with a first tag. Continuously tracking, based on the first tag, the first flow of the multi-tenant VPN. Capturing one or more characteristics of the first flow of the multi-tenant VPN. Categorizing the first flow of the multi-tenant VPN based on the one or more characteristics of the first flow. Providing the categorization of the first flow to a first tenant of the multi-tenant VPN. Receiving, based on input from the first tenant and the categorization of the first flow, a first policy. Enforcing the first policy on the first flow based on the first tag of the first flow and the continuous tracking of the first flow.

Abstract: Techniques and mechanisms for identifying unmanaged cloud resources with endpoint and network logs and attributing the identified cloud resources to an entity of an enterprise that owns the cloud resources. The process collects data from sources, e.g., endpoint and network logs, with respect to traffic in a computer network and based at least in part on the data, extracts relationships related to the traffic. The process applies rules to the relationships to extract destinations in the computer network that provide cloud resources in a cloud environment, wherein the cloud resources are owned by an enterprise. One or more users or business entities of the enterprise are identified as accessing the cloud resources.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

Abstract: The present disclosure is directed to associating computing devices with each other based on computer network activity for selection of content items as part of an online content item placement campaign. A first linking factor is identified based on a connection between a first device and the computer network via a first IP address during a first time period, and based on a connection between a second device and the computer network via the first IP address during the first time period. A number of devices that connect with the computer network via the first IP address is determined. A positive match probability is generated. A second and third linking factors are monitored. A negative match probability is determined based on the second and third linking factors. The first device is linked with the second device based on the positive and negative match probabilities.