Abstract: Systems, methods, and related technologies for generating a network system map based on network traffic and possibly additional data are described. Network traffic may be received and parsed to obtain metadata associated with the network traffic. A network system may be identified based on the metadata. A network system map may be generated for the network system based on one or more of the metadata or the additional data.

Abstract: A computing device is provided, including a processor configured to receive a selection of a performance profile of a plurality of performance profiles. The performance profile may indicate a performance variable goal state for a first network stack component included in a network stack of a plurality of network stack components. In response to receiving the selection of the performance profile, the processor may be further configured to control a network performance parameter of the first network stack component such that a performance variable of the first network stack component approaches the performance variable goal state. The processor may be further configured to transmit, to an additional network stack component of the plurality of network stack components, instructions to control an additional network performance parameter of the additional network stack component such that the performance variable of the first network stack component approaches the performance variable goal state.

Abstract: A system for obtaining information about an Internet of Things (IoT) device connected to a network includes a data traffic collection point, a data processing module and an artificial intelligence module. The data traffic collection point collects data units communicated to or from a specified IoT device of a plurality of IoT devices connected to a network. The data processing module processes quantitative information pertaining to the data units communicated to or from the specified IoT device collected over a defined time interval, to create a temporal data traffic fingerprint of the specified IoT device. The artificial intelligence module utilizes a machine learning model to deduce device identifying information of the specified IoT device from the temporal data traffic fingerprint of the specified IoT device.

Abstract: Networks and methods are provided for use in directing retry requests for content based on intervals to retry defined by network conditions. One example method generally includes receiving, from a computing device, an application programming interface (API) request for content and determining whether the API request exceeds a predefined rate limit of API requests. The method then includes, in response to the API request exceeding the predefined rate limit, calculating a retry interval for the API request based on the predefined rate limit of API requests and a number of expected API requests for an upcoming interval, appending the retry interval to a failure notice, and transmitting the failure notice to the computing device thereby indicating to the computing device to retry the API request based on the retry interval rather than immediately or rather than at another preset interval of the computing device.

Abstract: According to one configuration, a wireless base station has access to a cache (repository) that stores a stream of content including multiple segments of content. The cache stores (caches) a first segment of content from the received stream of content. The first segment of content is cached in the repository for a window of time during which the first segment of content is temporarily available from the wireless base station. In response to receiving a respective request from each mobile communication device in a group of multiple mobile communication devices requesting the first segment of content during the window of time, a wireless base station communicates the first segment of content from the cache to each mobile communication device in the group.

Abstract: A method for arbitrarily grouping computer system resources includes receiving a resource policy to be applied to a group of resources associated with a user. The resource policy includes a unique user-defined label assigned to each resource of the group of resources. The method also includes identifying the group of resources among a plurality of resources using the user-defined label. Here, the plurality of resources is associated with the user and distributed across a plurality of different computing environments. For each identified resource of the group of resources, the method also includes applying the resource policy to a corresponding identified resource by accessing the respective computing environment where the corresponding identified resource resides.

Abstract: Techniques are disclosed for generating device cluster capability information for a cluster of devices in a network environment. Capability information can specify capabilities of the devices in the cluster. A first user device can generate device capabilities for the first user device and obtain device capabilities for other devices in the cluster. The first user device can generate cluster capability information providing an intersection of the first set of device capabilities and device capabilities of the other user devices in the cluster. The first user device can obtain cluster capability information for other clusters in the network environment and receive a request from a service user device to perform a specific task. The first user device can transmit cluster capability information relating to a selected cluster that corresponds with the request.

Abstract: Systems and methods are described for managing a user device in multiple management modes. In an example, an agent executing on the user device can enroll the user device with a Unified Endpoint Management (“UEM”) system in a limited management mode. The agent can receive and install a first configuration profile from a server that configures the agent to operate on an unmanaged channel of the user device. In one example, the user device can have a third-party management client that manages the user device on a managed channel. The third-party management client can be removed. The agent can enroll the user device with the UEM in a full management mode. The agent can receive and install a second configuration profile that configures the agent to operate on the managed channel of the user device.

Abstract: Provided are methods and systems for managing, distributing, and publishing content. An exemplary method can comprise receiving, in a content management system, an asset for distribution. The method can also comprise generating a metadata object according to a submission standard or requirement of a destination asset distribution platform. The destination asset distribution platform can be external to the content management system and the metadata object can comprise data indicative of or associated with the asset.

Abstract: Systems, methods, and other embodiments associated with autonomous cloud-node scoping for big-data machine learning use cases are described. In some example embodiments, an automated scoping tool, method, and system are presented that, for each of multiple combinations of parameter values, (i) set a combination of parameter values describing a usage scenario, (ii) execute a machine learning application according to the combination of parameter values on a target cloud environment, and (iii) measure the computational cost for the execution of the machine learning application. A recommendation regarding configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment to execute the machine learning application is generated based on the measured computational costs.

Abstract: Systems and methods for creating network topology plans are provided. A method, according to one implementation, includes receiving a first constraint (number of nodes) to be set in a graph (under construction). The graph, when complete, is configured to include nodes and interconnections so as to enable each node to reach any other node via one or more interconnections. The method also includes receiving a second constraint (number of degrees) to be set with respect to each node and which defines the maximum number of interconnections that can be connected to each node. Upon determining that the graph is developed to an extent where each node is connected to at least one interconnection and every node is reachable by any other node, the method further includes performing functions to improve metrics related to a hop count that represents a number of interconnections needed for one node to reach another.

Abstract: An information processing system including Application Platform capable of communicating with Edge1 connected to each other to be able to communicate each other, in which Application Platform includes a second processor, information on microservices and data possessed by Edge1, and performance information describing the performance of Edge1, and the second processor uses predetermined data to combine a plurality of predetermined microservices and causes Edge1 to execute them in a predetermined order. When executing the application, microservices and data are moved between Edge1 based on the information of the microservices and the data possessed by Edge1, and the performance information.

Abstract: Systems and methods are disclosed to implement a time series anomaly detection system that uses configurable statistical control rules (SCRs) and a forecasting system to detect anomalies in a time series data (e.g. fluctuating values of a network activity metric). In embodiments, the system forecasts future values of the time series data along with a confidence interval based on seasonality characteristics of the data. The time series data is monitored for anomalies by comparing actual observed values in the time series with the predicted values and confidence intervals, according to the SCRs. The SCRs may be defined and tuned via a configuration interface that allows users to visually see how different SCRs perform over real data. Advantageously, the disclosed system allows users to create custom anomaly detection triggers for different types of time series data, without use of a monolithic detection model which can be difficult to tune.

Abstract: Deep reinforcement learning is applied to self-orchestration in edge device computing for offloading within a spatial network community to reduce latency and bandwidth issues. A revised online policy gradient training algorithm based on importance sampling in addition to the use of DRL-based offloading provides for continued use of original sample training data. A request for help scheme supports edge-device cooperation among neighboring devices of the spatial network community by sharing edge device state information (EDSI) for governing task assignments.

Abstract: A method of reserving hardware resources for management appliances of a software-defined data center (SDDC) that have been deployed onto one or more hosts of a cluster of hosts, includes reserving hardware resources of the cluster for a resource pool that has been created for the management appliances, the hardware resources including at least processor resources of the hosts and memory resources of the hosts, and assigning the management appliances to the resource pool created for the management appliances. The management appliances share the hardware resources of the cluster with one or more other resource pools and, after the steps of reserving and assigning, are allocated at least the hardware resources that have been reserved for the resource pool created for the management appliances.

Abstract: Techniques for distribution of application logic in digital networks are disclosed. In one embodiment, the techniques may be realized as a method that includes receiving a request from a first device to initiate a service instance associated with a service. The service instance is associated with application logic. The method further includes determining one or more parameters associated with the request, identifying at least a portion of the application logic to distribute to a mobile edge server based on the one or more parameters, and distributing the at least a portion of the application logic to the mobile edge server to cause the mobile edge server to provide the service instance to the first device.

Abstract: Described herein are systems, methods, and software to manage workflow monitoring between a client and one or more web servers. In one implementation, a client may initiate a workflow and maintain timing information associated with HTTP requests to one or more web servers as part of the workflow. The client further obtains, from the one or more web servers, log information for operations initiated from the HTTP requests to the one or more web servers and generates a timing diagram display based on the timing information and the log information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to coordinate node level adaptations. An example apparatus includes an adaptation support determiner to determine if an adaptation in an adaptation message is supported by a first device, an extractor to, in response to the determination that the adaptation in the adaptation message is supported by the first device, calculate a start-time for the first device based on (a) a transit duration of the adaptation message, (b) an execution duration of the adaptation in the adaptation message, and (c) a timestamp of when the second device sent the adaptation message, and an initiate a timer value for the first device and the second device, the timer value being a function of the start-time. The example apparatus further includes an installer to, in response to the timer value satisfying a threshold, execute the adaptation to reduce disruptions in the CPS.

Abstract: A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: providing a data center asset with a data center asset bridging module, the data center asset comprising a data center asset user interface; establishing access to the data center asset; when access to the data center is established, enabling a connection between a cloud-based data center monitoring and management console and the data center asset via the data center asset bridging module; and, exchanging information between the data center asset and the cloud-based data center monitoring and management console via the bridging module.

Abstract: Example techniques relate to changing a playback device mode based on a device base. In an example implementation, a first playback device operates in a first mode where it is connected to a first network and plays back audio content while in the first mode. The playback device detects connection of the first playback device to a device base and while the first playback device is on the device base, detects connection to a second network. The first playback device switches from operating in the first mode to operating in a second mode. The first playback device forms a stereo pair configuration with a second playback device over the second network in the second mode. While in the second mode, the first playback device plays back a first channel of audio content in synchrony with the second playback device playing back a second channel of the audio content.