Provided herein are systems and methods for determining relationships between events occurring in networks. Notifications describing events occurring in networks can be received and processed to determine groups of network event types. A root-cause network can be generated based on the events, with the nodes of the root-cause network representing different event types and the edges of the root-cause network indicating directional, causal relationships between the nodes. A received network event can be processed to determine potential causes of the received network event based on the root-cause network and other events received by the network.

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

A method for providing dependency registration and impacted service notification is provided. The method includes mining resource data for a plurality of resources included in heterogeneous platforms included in a single eco-system, and identifying, for each resource, one or more connections with other resources present in the heterogeneous platforms based on the mined resource data. The method also includes generating a resource dependency chain from the resource data and the identified one or more connections, and monitoring change events for the resources included in the heterogeneous planforms. When a change event is detected for a target resource among the resources, the method includes determining one or more resources that are impacted by the change event detected for the target resource based on the resource dependency chain, and transmitting a notification to users of the impacted resources.

A method includes executing, on a processor of a computing device, instructions that cause the computing device to perform operations. The operations include executing, on a processor of a computing device, instructions that cause the computing device to perform operations. The operations include receiving site features associated with a communication site and receiving event features associated with a potential outage-causing event. A classifying engine is employed to generate an impact metric indicating an effect on the communication site from the potential outage-causing event based on the site features and the event features.

Systems and methods for failover port forwarding between peer storage nodes are described. Storage nodes may include separate data ports for host network communication and peer network communication. In the event of host port failure, host nodes may be configured to send failover storage requests to a different storage node and that storage node may forward the failover storage request through the peer ports to reach the target storage node.

A communication system includes a first communication device and a second communication device. The first communication device includes a first processor that executes a first process including: performing a logging process for recording an operation of the first communication device; monitoring whether an abnormal occurrence is present and suspending the logging process when the abnormal occurrence is detected; and generating a trigger signal that instructs to suspend the logging process when the abnormal occurrence is detected, and a transmitter that transmits the trigger signal. The second communication device includes a receiver that receives the trigger signal transmitted from the first communication device, and a second processor that executes a second process including: performing a logging process for recording an operation of the second communication device; and suspending the logging process when the trigger signal is received by the receiver.

Analysis of a root cause of errors within a cloud network is manually complex and computationally intensive. Methods and systems are provided to determine a subset of elements of the cloud network to analyze, and to identify a subset of analyzers for analyzing the subset of elements to determine the root cause for the error. Thus, when configuring a network, a user may be provided with an identification of the root cause of error, enabling the user to quickly identify and correct the error.

Disclosed herein are system, method, and computer program product embodiments for optimal placement of aggregator equipment in telecommunication networks. An embodiment operates by determining an optimized traffic aggregation configuration based on execution of a network optimization function. The embodiment executes the network optimization function by receiving, as an input, information corresponding to resource capacity of the one or more types of aggregator units and resource demands generated by the plurality of access nodes, calculating an estimated number of each type of traffic aggregator unit of the one or more types of traffic aggregator units, and calculating the optimized traffic aggregation configuration. The embodiment modifies, based on the optimized traffic aggregation configuration, a network connectivity configuration corresponding to the plurality of network sites, and routes, based on the modified network connectivity configuration, network traffic via the optimized group of one or more types of traffic aggregator units.

Systems and methods are provided for predicting system or network failures, such as a degradation in the services provided by a service provider system or network, a system or network outage, etc. In a discovery phase, failure scenarios that can be accurately predicted based on monitored system events are identified. In an operationalization phase, those failure scenarios can be used to design production run-time machines that can be used to predict, in real-time, a future failure scenario. An early warning signal(s) can be sent in advance of the failure scenario occurring.

The subject matter described herein provides systems and techniques for a network planning and optimization tool that may allow for network capacity planning using key network failures for an arbitrary pair of network topology and demands. Performing network capacity planning with key network failures, instead of using other techniques, may avoid over-building the topology of a network. In particular, key network failures may be generated from the probabilistic failures, and the impact of these failures on a network may be computed. Expected flow availability SLO or a function thereof may be computed, using this information, and used by the tool to design a robust network. With an embedded flow availability calculation and updated risk framework, the capacitated cross-layer network topologies output by the tool may meet network demands/flows with their respective SLO type at the lowest cost.