H04L41/147

Predictive overlay network architecture

The predictive overlay network architecture of the present invention improves the performance of applications distributing digital content among nodes of an underlying network such as the Internet by establishing and reconfiguring overlay network topologies over which associated content items are distributed. The present invention addresses not only frequently changing network congestion, but also interdependencies among nodes and links of prospective overlay network topologies. The present invention provides a prediction engine that monitors metrics and predicts the relay capacity of individual nodes and links (as well as demand of destination nodes) over time to reflect the extent to which the relaying of content among the nodes of an overlay network will be impacted by (current or future) underlying network congestion. The present invention further provides a topology selector that addresses node and link interdependencies while redistributing excess capacity to determine an overlay network topology that satisfies application-specific performance criteria.

Predictive overlay network architecture

The predictive overlay network architecture of the present invention improves the performance of applications distributing digital content among nodes of an underlying network such as the Internet by establishing and reconfiguring overlay network topologies over which associated content items are distributed. The present invention addresses not only frequently changing network congestion, but also interdependencies among nodes and links of prospective overlay network topologies. The present invention provides a prediction engine that monitors metrics and predicts the relay capacity of individual nodes and links (as well as demand of destination nodes) over time to reflect the extent to which the relaying of content among the nodes of an overlay network will be impacted by (current or future) underlying network congestion. The present invention further provides a topology selector that addresses node and link interdependencies while redistributing excess capacity to determine an overlay network topology that satisfies application-specific performance criteria.

Systems and method for providing an ontogenesis intelligence engine

Systems and methods for controlling operations of a computer system. The methods comprises: collecting, by at least one computing device, information about events occurring in the computer system; performing automated ontogenesis operations by the at least one computing device using the collected information to determine a context of a given situation associated with the computer system, define parameters for a plurality of different sets of actions that could occur in the context of the given situation, and simulate the sets of actions to generate predicted consequences resulting from the performance of certain behaviors by nodes of the computer system; and using the parameters of at least one of the predicted consequences to control operations of the computer system.

Systems and method for providing an ontogenesis intelligence engine

Systems and methods for controlling operations of a computer system. The methods comprises: collecting, by at least one computing device, information about events occurring in the computer system; performing automated ontogenesis operations by the at least one computing device using the collected information to determine a context of a given situation associated with the computer system, define parameters for a plurality of different sets of actions that could occur in the context of the given situation, and simulate the sets of actions to generate predicted consequences resulting from the performance of certain behaviors by nodes of the computer system; and using the parameters of at least one of the predicted consequences to control operations of the computer system.

Enhanced selection of cloud architecture profiles

This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation. An updated cloud architecture profile is generated based on the simulated utilization.

Tool registry for DevOps toolchain automation

The present invention extends to methods, systems, and computer program products for tool registry for automating DevOps toolchains. Submission of a DevOps tool, authentication information, and tool configuration data is received from a user. Subsequently, the DevOps tool is selected for inclusion in a DevOps job. The DevOps tool including the authentication information and tool configuration data is automatically accessed from the tool registry. The DevOps tool is configured in accordance with the accessed authentication information and accessed tool configuration data as part of the DevOps job and for interaction with the one or more other DevOps tools. The DevOps job is deployed.

LEARNING SLA VIOLATION PROBABILITY FROM INTELLIGENT FINE GRAINED PROBING

In one embodiment, a device obtains a first set of measurements of a path metric for a path in a network that are measured using periodic probing of the path. The device obtains a second set of measurements of the path metric for the path that are measured using fine-grained probing of the path at a higher frequency than that of the periodic probing. The device generates a predictive model that predicts values of the path metric, based on the first set of measurements and on the second set of measurements. The device causes, based on a value of the path metric predicted by the predictive model, traffic to be rerouted from the path to another path in the network.

CANCELING PREDICTIONS UPON DETECTING CONDITION CHANGES IN NETWORK STATES

In one embodiment, a device obtains an indication of a network event predicted by a routing engine for a network. The device initiates monitoring of one or more network paths associated with the network event, to determine one or more states of the network. The device makes a comparison between the one or more states of the network and a set of one or more constraints. The device provides a prediction cancelation notification to the routing engine, based on the comparison.

CANCELING PREDICTIONS UPON DETECTING CONDITION CHANGES IN NETWORK STATES

In one embodiment, a device obtains an indication of a network event predicted by a routing engine for a network. The device initiates monitoring of one or more network paths associated with the network event, to determine one or more states of the network. The device makes a comparison between the one or more states of the network and a set of one or more constraints. The device provides a prediction cancelation notification to the routing engine, based on the comparison.

SECURE REMOTE ACCESS TO HISTORICAL DATA
20230216831 · 2023-07-06 ·

Methods, systems and computer products provide access to historical data over a real-time tunnel in an architecture including an operational technology (OT) network, a de-militarized zone (DMZ) and an information technology (IT) network. The OT network interleaves real-time data and historical data over a first tunnel connection, a first firewall and a second firewall in conjunction with a DMZ and an IT network by (a) performing pull replication of the historical data, (b) daisy chaining the historical data, or (c) a combination of (a) and (b).