Methods and systems that use a plurality of machine learning models to both monitor user-generated data entries corresponding to differences in network traffic that may be evidence of a disruption and determine severity levels based on: (i) current and historic differences in average network traffic over the plurality of communication networks; (ii) current and historic user-generated data entries; and (iii) labeled severity levels for historic differences in average network traffic over the plurality of communication networks.

A processing system including at least one processor may generate a plurality of subsequences of a time series data set, convert the plurality of subsequences to a plurality of frequency domain point sets, compute pairwise distances of the plurality of frequency domain point sets, project the plurality of frequency domain point sets into a lower dimensional space in accordance with the pairwise distances, where the projecting maps each of plurality of frequency domain point sets to a node of a plurality of nodes in the lower dimensional space, and generate a notification of at least one isolated node of the plurality of nodes, where the at least one isolated node represents at least one anomaly in the time series data set.

A data-driven approach to network performance diagnosis and root-cause analysis is presented. By collecting and aggregating data attribute values across multiple components of a content delivery system and comparing against baselines for points of inspection, network performance diagnosis and root-cause analysis may be prioritized based on impact on content delivery. Recommended courses of action may be determined and provided based on the tracked network performance analysis at diagnosis points.

A method of responding to an operation, an electronic device and a storage medium are provided, which relate to a field of cloud computing, and in particular to a field of cluster technology. The specific implementation solution includes: performing, in response to determining that a target operation performed by a target client on a shared resource has timed out, a fault detection on the target client to obtain a fault detection result; and implementing, in response to determining that the fault detection result represents that the target client has a fault, an update operation to obtain a target authority identifier, so that the target client is prevent from continuing to perform the target operation by using the target authority identifier.

A computer-implemented method and a computer system for identifying persistent anomalies for failure prediction. The computer system receives a time series data stream. The computer system received a predetermined number N and a predetermined number M which is a fraction of N. The computer system segments the time series data stream into N consecutive sliding windows. The computer system performs supervised persistent anomaly detection to determine whether anomalies across the N consecutive sliding windows are persistent, by using a binary classification model. The computer system performs unsupervised persistent anomaly detection to determine whether the anomalies across the N consecutive sliding windows are persistent. The computer system combines results of the supervised persistent anomaly detection and results of the unsupervised persistent anomaly detection to determine persistent anomalies.

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.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring data center assets within a data center; identifying an issue within the data center, the issue being associated with an operational situation associated with a particular component of the data center; associating the issue with a particular point in time; and, informing a user about the issue, the informing including information regarding the particular point in time, the informing including a graphical depiction of the particular component of the data center and the issue within the data center.

Described is a method and system for latency measurement in communication systems. The method comprises: determining, by a first communication device, a power-management state of a second communication device; transmitting, by the first communication device, one or more packets to the second communication device over a communication link, the one or more packets to be received by the second communication device while in the power-management state; receiving, from the second communication device over the communication link, one or more response packets in response to the one or more packets; and determining a latency of the communication link when the second communication device is in the power-management state based on the one or more packets and the one or more response packets.

Provided are a system, method, and computer program product for network anomaly detection. The method includes receiving event data associated with a plurality of events in a computer network. The method also includes determining nested groups of the event data representing tiers of an operational hierarchy. The method further includes generating display data to show a graphical representation of the event including a plurality of nested graphical nodes and at least one spline. Each graphical node is associated with a group or a computer node, each graphical node encompasses and/or is encompassed by another graphical node, a size of each graphical node is proportional to an aggregated parameter value of events associated therewith, each spline connects at least two graphical nodes and includes a curve that passes through a common graphical node, and each spline is associated with a communication between at least two computer nodes.

A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.