Dell Products Inc.

Siva Rama Krishna Kottapalli

Karthik Hubli

Bina K Thakkar

Peter Beale

Wenjin Liu

An apparatus comprises a processing device configured to obtain performance data for a plurality of workloads, to select a subset of the performance data corresponding to a subset of the plurality of workloads having a given workload type, and to generate a model characterizing an expected performance of the given workload type by analyzing the selected subset of the performance data to estimate a queuing curve characterizing the expected performance of the given workload type. The processing device is also configured, responsive to determining that a quality of the generated model is above a designated threshold quality level, to utilize the generated model to identify performance impacting events for a given workload of the given workload type and to modify provisioning of compute, storage and network resources allocated to the given workload responsive to identifying performance impacting events for the given workload of the given workload type.

Performance impact detection using queuing curve analysis

The described technology is generally directed towards processing structured data corresponding to system information such as alerts, logs, events, health check data and the like, to identify stateful flows from the structured data. Identical flows are combined, similar flows, based on similarity scores obtained from stateful flows are combined, and incremental stateful flows are combined. Neural networks can be used to identify the similar flows and incremental flows. A distribution can be obtained based on counts of the different stateful flows that remain after combining the identical, similar and incremental stateful flows; a neural network that accounts for subtle differences can be used to provide a more accurate distribution than simple counts. Anomalous stateful flows can be identified from the distribution, with some action taken for an anomalous stateful flow, e.g., to send a notification or other output to a support engineer of the like for proactive issue detection.

Structured data flow identification for proactive issue detection

Methods and systems for managing deployments are disclosed. A deployment may include one or more devices. The devices may include hardware and/or software components. The operation of the deployment may depend on the operation of these devices and components. For example, improper operation of any of these devices and/or components may impair (e.g., reduce performance, reduce functionality, etc.) the operation of the deployment. To manage the operation of the deployment, the system may include a deployment manager. The deployment manager may obtain logs for components of the deployment reflecting the historical operation of these components and use the logs to predict the future operation of the deployment. If it is determined that a deployment is likely to become impaired in the future, then the deployment manager may take proactive action to reduce the likelihood of the deployment entering the impairment state.

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North Chelmsford, MA, United States of America

Siva Rama Krishna Kottapalli