Netapp, Inc.

Leon Fairbanks

Curtis E Hrischuk

Kevin Faulkner

Siu Wu

Alma Dimnaku

Vinod Vasant Pai

George Smirnov

Kenneth Hu

David Kaeli

Oguz Semerci

Abdul Basit

Phil Larson

Ulhas Pai

Steven B Boswell

Methods and systems for a networked storage system are provided. One method includes filtering performance data associated with a resource used in a networked storage environment for reading and writing data at a storage device; and determining available performance capacity of the resource using the filtered performance data. The available performance capacity is based on optimum utilization of the resource and actual utilization of the resource, where utilization of the resource is an indicator of an extent the resource is being used at any given time, the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency and latency is an indicator of delay at the resource in processing the workload.

Methods and systems for determining performance capacity of a resource of a networked storage environment

Methods and systems for a network storage environment are provided. One method includes retrieving stored performance data associated with a resource used in the networked storage environment, where the stored performance data includes latency data, utilization data and a service time; filtering the stored performance data by removing any observations that are beyond a certain value of the service time; grouping the filtered stored performance into utilization bins and identifying a representative of each utilization bin; generating by the processor a hybrid latency versus utilization curve comprising a first portion that is based on the representative of each utilization bin and a second portion generated using a model based technique and determining by the processor available performance capacity of the resource using the hybrid latency versus utilization curve; where the available performance capacity is based on optimum utilization of the resource and actual utilization of the resource.

The present invention provides methods, systems, apparatus, and computer software/program code products adapted for operating in, or in conjunction with, an otherwise conventional computing system, and which enable evaluating, monitoring and predicting the performance of computer systems and individual elements or groups of elements within such computer systems.

Evaluating and predicting computer system performance using kneepoint analysis

Methods and systems for inter-cluster storage system monitoring and analysis are provided. The method includes monitoring a non-volatile memory delay center for a first storage cluster having a first node and a second node configured to operate as a first high availability pair, where data for a write request to write data to the first node is also written to the second node as well as to a second cluster having a third node and a fourth node, where the third node and the fourth node are also configured to operate as a second high availability pair to store the data for the write request at one or both of the third and fourth node. The non-volatile memory delay center is used to monitor and detect latency due to any delay caused by a non-volatile memory of the first node used as a write cache.

Method and system for monitoring and analyzing quality of service in a metro-cluster

Methods and systems for managing resources in a networked storage environment are provided. One method includes generating a relationship between latency and utilization of a resource in a networked storage environment using observation based, current and historical latency and utilization data, where latency is an indicator of delay at the resource for processing any request and utilization of the resource is an indicator of an extent the resource is being used at any given time; and selecting an optimal point for the generated relationship between latency and utilization, where the optimal point is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency.

Methods and systems using observation based techniques for determining performance capacity of a resource of a networked storage environment

Methods and systems for monitoring quality of service (QOS) data for a plurality of storage volumes are provided. QOS data is collected for the plurality of storage volumes and includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request. An expected range for future QOS data based on the collected QOS data is generated. The process then determines a deviation of each potential bully storage volume of a resource used by any victim storage volume, where the deviation of each bully storage volume is based on a number of current I/O requests (IOPS) that are processed by each potential bully storage volume, a forecasted value of TOPS and a predicted upper threshold TOPS value for each potential bully storage volume; and filters the potential bully storage volumes based on an impact of each potential bully storage volume.

Method and system for monitoring and analyzing quality of service in a storage system

Methods and systems for monitoring quality of service (QOS) data for a plurality of storage volumes are provided. QOS data is collected for the plurality of storage volumes and includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request. The process determines an average of N collected QOS data points at any given time; and iteratively analyzes each QOS data point to detect if a step-up or a step-down function has occurred, where a step-up function represents an unpredictable increase in value of a data point and a step-down function is an unpredictable decrease in value of the data point. A subset of the N QOS data points based on when the step-up function or step-down function occurs is selected for analysis and an expected range for future QOS data based on the subset of the N QOS data points is generated.

Methods and systems for monitoring quality of service (QOS) data for a plurality of storage volumes from a storage operating system of a storage system are provided. A performance manager collects the QOS data from the storage operating system and the QOS data includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request. An expected range for future QOS data is generated based on the collected QOS data. The QOS data is monitored for each storage volume for determining whether a current QOS data for each storage volume is within the expected range.

Methods and systems for identifying a victim storage volume from among a plurality of storage volumes based on a comparison of current Quality of Service (QOS) data with a dynamic threshold value that is based on historical QOS collected data for the plurality of storage volumes are provided. A performance manager collects the current and historical QOS data from a storage operating system of the storage system, which includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request. The current and historical QOS data for the resources used by the victim storage volume are retrieved and compared with the current QOS data of each resource to an expected range based on the historical QOS data. Another storage volume is identified as a bully when its usage of a resource in contention contributes to creating the victim storage volume.

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Winchester, MA, United States of America

Leon Fairbanks