Techniques are disclosed relating to creating and managing an information flow within a multi-layer computer network. In various embodiments, a computer system in a first layer within a multi-layer computer network, maintains state information defining an information flow within the multi-layer computer network. In various embodiments, the computer system assigns a particular action (that is included in the information flow) to be performed at a second, different layer of the multi-layer computer network. In various embodiments, the computer system generates program instructions to perform the particular action. The program instructions may be generated using device information accessible to the computer system and indicative of characteristics of a computer system within the second layer. In various embodiments, the computer system in the first layer causes the program instructions to be sent to the computer system in the second layer to perform the particular action as part of the defined information flow.

Techniques are disclosed relating to creating and managing an information flow within a multi-layer computer network. In various embodiments, a computer system in a first layer within a multi-layer computer network, maintains state information defining an information flow within the multi-layer computer network. In various embodiments, the computer system assigns a particular action (that is included in the information flow) to be performed at a second, different layer of the multi-layer computer network. In various embodiments, the computer system generates program instructions to perform the particular action. The program instructions may be generated using device information accessible to the computer system and indicative of characteristics of a computer system within the second layer. In various embodiments, the computer system in the first layer causes the program instructions to be sent to the computer system in the second layer to perform the particular action as part of the defined information flow.

In one example, the present disclosure describes a device, computer-readable medium, and method for scaling network capacity predictively, based on customer interest. For instance, in one example, a method includes predicting an interest of a first customer in data content that will be available for consumption over a data network at a time in the future, wherein the predicting is based on customer data including at least a search pattern associated with the first customer, flagging the data content when the predicting indicates at least a threshold degree of likelihood that the first customer will be interested in the data content, and scaling an allocation of resources of the data network to the first customer, based on the flagging.

Techniques are provided for allocation of shared computing resources using source code feature extraction and machine learning techniques. An exemplary method comprises obtaining source code for execution in a shared computing environment; extracting a plurality of discriminative features from the source code; obtaining a trained machine learning model; and generating a prediction of an allocation of one or more resources of the shared computing environment needed to satisfy one or more service level agreement requirements for the source code. The generated prediction is optionally adjusted using a statistical analysis of an error curve, based on one or more error boundaries obtained by the trained machine learning model. The trained machine learning model can be trained using a set of discriminative features extracted from training source code and corresponding measurements of metrics of the service level agreement requirements obtained by executing the training source code on a plurality of the resources of the shared computing environment.

Techniques for facilitating messaging in a group-based communication system between a calling device and one or more client devices. A first user operating a calling device may send a request to a call server computing device to send a call-based communication via the group-based communication system. The call-based communication may include voice or textual data. The call server computing device may receive the call-based communication and may format the call-based communication to send to the group-based communication system. The call server may send the formatted call-based communication to the group-based communication system for presentation via the one or more client devices.

A connectivity enablement device includes one or more processors, one or more memories and a hardware input port. The memories store program instructions that when executed examine a token obtained from a token transfer device inserted into the port, and cause one or more messages to be transmitted to a virtualized computing service. The messages indicate (a) the connectivity enablement device, (b) the token transfer device, (c) the token's source and (d) a server. An indication that the server has been configured within an isolated virtual network is obtained at the connectivity enablement device.

A connectivity enablement device includes one or more processors, one or more memories and a hardware input port. The memories store program instructions that when executed examine a token obtained from a token transfer device inserted into the port, and cause one or more messages to be transmitted to a virtualized computing service. The messages indicate (a) the connectivity enablement device, (b) the token transfer device, (c) the token's source and (d) a server. An indication that the server has been configured within an isolated virtual network is obtained at the connectivity enablement device.

A recovery workflow is part of an automated management service for bare metal hosts allocated for single-tenant operation in a multi-tenant environment. The health of the hosts is monitored using a set of health criteria. If it is detected that one of the host machines fails a health check then a host recovery workflow can be initiated. As part of the workflow, the failed host can be repurposed or retired. A spare host class can be used to obtain a new host to take over for the failed host. Once deployed, the operation of the new host can be tested. Upon passing the test, the new host can take over for the failed host. A new host resource can be automatically requested to be added to the spare host class in order to ensure that there are sufficient resources available in case of an additional failure.

The disclosed systems and methods are for scheduling web-resources for a malicious data comprising receiving requests to publish web-resources, and generating a graph structure comprising: a parent nodes associated with parent web-resources, a zero-vertex node being connected with the of parent nodes via zero-vertex edges, child nodes associated with child web-resources, and web-resource edges connecting the parent nodes, child nodes and sub-child nodes. Using the graph structure, assigning, to a given child node a first assigned trust score based on a lowest trust score of a subset of parent nodes connected to the given child node in the graph structure such that a web-resource edge length connecting the given, child node and the subset of parent nodes is a representative of the first assigned trust score, applying a shortest-path algorithm for calculating a shortest-path and based on the shortest-path determining at least one web-resource prioritized for the malicious data assessment.

A particular overall architecture for transmission over a bonded channel system consisting of two interconnected MoCA (Multimedia over Coax Alliance) 2.0 SoCs (Systems on a Chip) and a method and apparatus for the case of a “bonded” channel network. With a bonded channel network, the data is divided into two segments, the first of which is transported over a primary channel and the second of which is transported over a secondary channel.