Disclosed herein are various embodiments to prevent, detect, or take action in response to the moving a device credential from one device to another, the improper configuration of a service processor, a missing service processor, or the tampering with a service processor in device-assisted services (DAS) systems.

Methods, systems and devices for network congestion control exploit the inherent burstiness of network traffic, using a wave-based characterization of network traffic and corresponding multiplexing methods and approaches.

Methods, systems and devices for network congestion control exploit the inherent burstiness of network traffic, using a wave-based characterization of network traffic and corresponding multiplexing methods and approaches.

Embodiments of the present application provide an acceleration management node. The acceleration management node separately receives acceleration device information of all acceleration devices. The acceleration device information includes an algorithm type, an acceleration bandwidth or non-uniform memory access architecture (NUMA). The acceleration management node obtains an invocation request from a client. The acceleration management node queries the acceleration device information to determine, from all the acceleration devices of the at least one acceleration node, a target acceleration device matching the invocation request. The acceleration management node further instructs a target acceleration node to respond to the invocation request.

Embodiments of the present application provide an acceleration management node. The acceleration management node separately receives acceleration device information of all acceleration devices. The acceleration device information includes an algorithm type, an acceleration bandwidth or non-uniform memory access architecture (NUMA). The acceleration management node obtains an invocation request from a client. The acceleration management node queries the acceleration device information to determine, from all the acceleration devices of the at least one acceleration node, a target acceleration device matching the invocation request. The acceleration management node further instructs a target acceleration node to respond to the invocation request.

A digital data communications network that supports efficient, scalable routing of data and use of network resources by combining a recursive division of the network into hierarchical sub-networks with repeating parameterized general purpose link communication protocols and an addressing methodology that reflects the physical structure of the underlying network hardware. The sub-division of the network enhances security by reducing the amount of the network visible to an attack and by insulating the network hardware itself from attack. The fixed bandwidth range at each sub-network level allows quality of service to be assured and controlled. The routing of data is aided by a topological addressing scheme that allows data packets to be forwarded towards their destination based on only local knowledge of the network structure, with automatic support for mobility and multicasting. The repeating structures in the network greatly simplify network management and reduce the effort to engineer new network capabilities.

A digital data communications network that supports efficient, scalable routing of data and use of network resources by combining a recursive division of the network into hierarchical sub-networks with repeating parameterized general purpose link communication protocols and an addressing methodology that reflects the physical structure of the underlying network hardware. The sub-division of the network enhances security by reducing the amount of the network visible to an attack and by insulating the network hardware itself from attack. The fixed bandwidth range at each sub-network level allows quality of service to be assured and controlled. The routing of data is aided by a topological addressing scheme that allows data packets to be forwarded towards their destination based on only local knowledge of the network structure, with automatic support for mobility and multicasting. The repeating structures in the network greatly simplify network management and reduce the effort to engineer new network capabilities.

A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

Non-limiting examples of the present disclosure describe proactive detection and notification of a potential loss of connectivity during an electronic collaborative communication. Subsequently, a state of the electronic collaborative communication is managed to improve, among other technical advantages, processing efficiency of associated computing devices and users involved in the electronic collaborative communication. A quality of a network feed for a participant in an electronic collaborative communication is identified and analyzed to generate a warning of potential loss of connectivity. A state of an electronic collaborative communication is managed relative to a continued quality evaluation of said network feed. For example, a network feed may be placed on hold and/or a communication suspended due to potential connectivity issues. A representation of a video feed may be updated and/or a communication resumed when a quality of that video feed is determined to satisfy a pre-selected quality threshold.

The present disclosure generally discloses a problem event prediction capability. The problem event prediction capability may be configured to predict various types of problem events (e.g., customer problems, customer tickets, customer outages, network problems, network tickets, network outages, or the like, as well as various combinations thereof) based on various types of asynchronous machine data (e.g., alarms, alerts, triggers, machine logs, machine messages, diagnostic logs, diagnostic messages, or the like, as well as various combinations thereof). The problem event prediction capability may be configured to generate a set of problem prediction rules based on historical machine data and to apply the problem prediction rules to observed machine data in order to predict various types of problem events.