Resource provider specifications, characterizing computing resources of computing resource providers, are received. The reachability of each IP address included in the received specification is determined. An agent is deployed that is operable to determine the value of each of a set of metrics in the environment of the host at which the agent is deployed. The agent determines the value of each metric of the set of metrics in the environment of the relevant host, and communicates the determined values to one or more computing devices that validate whether the resources characterized by the communicated values are sufficient to provide the performance characterized by the received specification and that each ISP router complies with a predetermined policy. For each computing resource provider validated and determined to comprise an ISP router compliant with policy, the specified computing resources are added to a pool of resources for cloud computing.

In some examples, a computing device may determine a prediction of a network outage of a network. The computing device may determine a priority of one or more data types expected to be received during the network outage. Further, the computing device may determine a latency category of the one or more data types expected to be received during the network outage. The computing device may store a data transmission rule for the one or more data types at least partially based on the priority and the latency category. The computing device may receive, from one or more data generators, during the network outage, data for transmission to the network. The computing device may transmit at least some of the received data to the network at least partially based on the data transmission rule.

A method includes polling, via a service specific manager operating on a software container in a cloud infrastructure, usage of different application resources and parameters for each service of a plurality of services provided in the cloud infrastructure to yield respective polled data for each service, collating, at the service specific manager, the respective polled data for each service to yield a collation, and based on the collation, deriving a respective weight for each service which a container manager can use to create multiple instances of a new service. The method further includes communicating the respective weight for each service to the container manager and determining, via the container manager, whether to scale up or scale down container services based on the respective weight for each service.

Embodiments are directed to monitoring network traffic in a network. A device relation model that may be comprised of two or more nodes and one or more edges stored in memory of the network computer may be provided to a network monitoring computer (NMC), such that each node represents an agent and each edge represents a relationship between two agents. If error signals are detected by the NMC, the NMC perform further actions to process the error signals. The device relation model may be traversed to identify agents associated with the error signals. The network traffic associated with the error signals and the agents may be analyzed by the NMC. If the error signals are associated with anomalies in the network traffic, users may be notified. The device relation model may be updated upon discovery of new computing devices, new applications, or new associations between agents.

A method and a node identification system for identifying at least one unknown mobile node in a communications network using details related to at least one known mobile node and organization of the details related to the at least one known mobile node. The method includes capturing details related to the at least one unknown mobile node and identifying an organization of the captured details related to the at least one unknown mobile node, comparing the details related to the at least one known mobile node and the organization of the details related to the at least one known mobile node with the captured details related to the at least one unknown mobile node and the organization of the captured details related to the at least one unknown mobile node, and determining a type of the at least one unknown mobile node based on the comparing.

A method for profiling network traffic. The method includes capturing, from the network traffic using a packet capturing device, a plurality of packets, identifying a first portion of the plurality of packets as a first flow based at least on a common Internet Protocol (IP) address assigned to each packet of the first flow by a network address translation (NAT) device, extracting, by a hardware processor separate from the NAT device and based on an NAT profile of the NAT device, a first data item from the first flow, wherein the first data item is inserted into the first flow by the NAT device for identifying a first host device coupled to the NAT device, and determining, by the hardware processor based on the first data item, that the first flow is generated by the first host device.

Techniques are disclosed for the identification of applications from communication sessions of network traffic between client devices and the generation of application-specific metrics for network traffic associated with the applications. In one example, a router obtains metrics for a plurality of packets. The router determines a session of a plurality of sessions associated with each packet. For each determined session, the router generates metrics for the session from the metrics of the packets associated with the session and determines an application of a plurality of applications associated with the session. For each determined application, the router generates metrics for the application from the metrics of the sessions associated with the application and transmits, to a device, the metrics for the application. In some examples, the router generates the metrics for each application on a per-client, per-next-hop, or per-traffic class basis.

The described technology provides a capability for web applications from different domains to interact within one application environment. For example, an enterprise web application executing on a client terminal is provided the capability to monitor a second web application from a third party vendor even when the second web application is independently executing within an iframe or the like within the enterprise web applications container or context. In some example embodiments, the communication is enabled by a composite cookie or key that incorporates portions of an enterprise web application cookie or key and also portions of a vendor web application cookie or key.

A performance indicator monitoring system (PIMS) receives a plurality of operational measurements (OMs) for each performance indicator (PI), which may be stored on a first storage. A rate of access by a monitoring system is determined for the PI. The rate may be determined, for example, related to a counter of the PI. An enriched dataset related to a subset of the plurality of OMs is generated, upon determination that the selected PI rate of use is higher than a primary threshold value. The generated enriched dataset may be stored in a second storage. In some embodiments, a component of the PIMS, such as a processing unit, is determined. A usage level of the component is then determined, and the enriched dataset is generated if the usage level of the component is below a secondary threshold.

Systems, methods and computer-readable storage devices each provide, for a given flow entry in a flow table associated with a packet flow, a first atomic counter associated with a first color and a second atomic counter associated with a second color. The system, for a first coloring interval, marks traffic in the packet flow from a tenant to a fabric as the first color and increments the first atomic counter. At a conclusion of the first coloring interval and for a second coloring interval, the system marks the traffic in the packet flow from the tenant to the fabric as the second color and increments the second atomic counter. The system compares first packet counts associated with the first color at an ingress point with second packet counts associated with the first color at an egress point to the fabric.