Abstract: Techniques are described for generating an end-to-end distributed trace in connection with a cloud or datacenter environment. In one example, a server obtains target application telemetry data and external telemetry data associated with one or more correlation identifiers included in one or more network communications provided to a target application in the cloud or datacenter environment. The server aggregates the target application telemetry data and the external telemetry data based on the one or more correlation identifiers to generate an end-to-end distributed trace associated with the one or more network communications.

Abstract: Techniques are described for generating an end-to-end distributed network trace involving cloud edge networks. In one example, a cloud or datacenter environment obtains, from an edge node in a cloud edge network, one or more network communications that include a correlation identifier associated with the one or more network communications and an identifier of the edge node. Based on the identifier of the edge node, the cloud or datacenter environment provides a network probe to the edge node. The cloud or datacenter environment obtains, from the edge node, telemetry data that is generated responsive to the network probe. The cloud or datacenter environment provides the telemetry data and the correlation identifier to an aggregation server that is configured to, based on the correlation identifier, aggregate the telemetry data with further telemetry data to generate an end-to-end distributed network trace associated with the one or more network communications.

Abstract: Techniques are described for generating an end-to-end distributed trace in connection with a cloud or datacenter environment. In one example, a server obtains target application telemetry data and external telemetry data associated with one or more correlation identifiers included in one or more network communications provided to a target application in the cloud or datacenter environment. The server aggregates the target application telemetry data and the external telemetry data based on the one or more correlation identifiers to generate an end-to-end distributed trace associated with the one or more network communications.

Abstract: Techniques are described for generating an end-to-end distributed trace in connection with a cloud or datacenter environment. In one example, a server obtains target application telemetry data and external telemetry data associated with one or more correlation identifiers included in one or more network communications provided to a target application in the cloud or datacenter environment. The server aggregates the target application telemetry data and the external telemetry data based on the one or more correlation identifiers to generate an end-to-end distributed trace associated with the one or more network communications.

Abstract: Techniques are described for generating an end-to-end distributed network trace involving cloud edge networks. In one example, a cloud or datacenter environment obtains, from an edge node in a cloud edge network, one or more network communications that include a correlation identifier associated with the one or more network communications and an identifier of the edge node. Based on the identifier of the edge node, the cloud or datacenter environment provides a network probe to the edge node. The cloud or datacenter environment obtains, from the edge node, telemetry data that is generated responsive to the network probe. The cloud or datacenter environment provides the telemetry data and the correlation identifier to an aggregation server that is configured to, based on the correlation identifier, aggregate the telemetry data with further telemetry data to generate an end-to-end distributed network trace associated with the one or more network communications.

Abstract: Data related to operational performance of a plurality of nodes in a system is obtained and a first metric anomaly associated with a node of the plurality of nodes in the system is identified. The first metric anomaly indicates that data associated with a first metric is outside a threshold range. Second metrics related to the first metric are identified and it is determined that one of the second metrics is an anomaly. Third metrics related to the second metric are identified and it is determined whether any third metric is an anomaly. The second metric is identified as a probable cause of the first metric anomaly when it is determined that no third metric is an anomaly. A report including information associated with the probable cause of the first metric anomaly is transmitted to a user device.

Abstract: Techniques are described for end-to-end network tracing involving external services. In one example, a synthetic agent identifies one or more external services that are involved in a process for interacting with a target application server. In response to identifying the one or more external services, the synthetic agent obtains telemetry data associated with the one or more external services. The synthetic agent correlates the telemetry data associated with the one or more external services and telemetry data associated with the target application server to generate an end-to-end network trace associated with the target application server.

Abstract: A method comprising: at a management entity configured to communicate with a network: upon detecting a performance problem on a network path in the network, generating a trigger probe having a correlation identifier, the trigger probe configured to transit the network path and, on one or more designated network nodes of the network path, trigger (i) capturing a full device state, including a control plane state and a data plane state, and (ii) exporting a report of the full device state with the correlation identifier; sending the trigger probe along the network path; receiving, from each of the one or more designated network nodes, the report that includes the correlation identifier and the full device state; and correlating each report to the performance problem based on the correlation identifier in each report, to diagnose a root cause of the performance problem using the full device state in each report.