Nutanix, Inc.

Cong Liu

Himanshu Shukla

Abhinay Nagpal

Jianjun Wen

Binny Sher Gill

Shyama Sundar Duriseti

Zihong Lu

Harry Hai Yang

Weiheng Chen

Surendran Madheswaran

Shuo Yang

Sourav Kumar

In accordance with some aspects of the present disclosure, an apparatus is disclosed. The apparatus includes a processor and a memory, wherein the memory includes programmed instructions that when executed by the processor, cause the apparatus to receive a request to join a plurality of entity data structures using a first join order, determine a first performance cost of the first join order, determine a second performance cost of a second join order, determine whether the second performance cost is lower than the first performance cost, in response to determining that the second performance cost is lower than or exceeds the first performance cost, select the second join order or the first join order, respectively, join the plurality of entity data structures using the selected join order, and send the joined plurality of entity data structures.

Intelligent query planning for metric gateway

A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.

Method for forecasting distributed resource utilization in a virtualization environment

Systems for alerting in computing systems. A method commences by defining a plurality of analysis zones bounded by respective ranges of system metric values, which ranges in turn correspond a plurality of system behavior classifications. System observations are taken while the computing system is running. A system observation comprising a measured metric value is classified into one or more of the behavior classifications. Based on the classification, one or more alert analysis processes are invoked to analyze the system observation and make a remediation recommendation. An alert or remediation is raised or suppressed based on one or more zone-based analysis outcomes. An alert is raised when anomalous behavior is detected. The system makes ongoing observations to learn how and when to classify a measured metric value into normal or anomalous behaviors. As changes occur in the system configuration, the analysis zones are adjusted to reflect changing bounds of the zones.

Computing system monitoring

Systems for computing cluster management. One embodiment commences upon receiving a set of observed workload parameters corresponding to one or more observable workloads that run in a computing cluster. While the workloads are running, workload stimulus and cluster response observations are taken and used to generate a workload resource usage predictive model based on mappings or correlations between the observable workloads parameters and observed resource usage measurements. A set of planned workloads are applied to the workload resource usage predictive model to predict a set of corresponding predicted resource usage demands. The predicted resource usage demands are then mapped to a set of recommended hardware to form resource deployment recommendations that satisfy at least some of the corresponding resource usage demands while also observing a set of hardware model compatibility constraints. The resource deployment recommendations that satisfy the set of hardware model compatibility constraints are displayed in a user interface.

Long-range distributed resource planning using workload modeling in hyperconverged computing clusters

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San Jose, CA, United States of America

Cong Liu