Key Metrics for Effective Storage Performance and Capacity Reporting - Backend Metrics (9 of 10)

Below are some metrics available on the back end storage array:

These are typical performance metrics showing throughput and response times, the type of thing you need to report on regularly so that you can be on top of performance before incidents start being generated.

Performance Capacity – Array Metrics

The key metrics that you need to get a handle on at volume level are throughput, response and latency.

Below is an example of NetApp metrics at volume level.

and below an example of metrics within EMC at the volume level.

The read/write ratio can give you an idea of what your work profile looks like.

Performance Capacity – Component Breakdown

The example below, using athene^®, shows a component breakdown for the server.

It’s essential to know whether you have any queuing going on (shown in yellow above), if queuing is happening you are exceeding the devices throughput rate.

In the final part of my blog series on Monday I’ll take a look at workload profiles, scorecards and dashboards.

Dale Feiste

Principal Consultant

Key Metrics for Effective Storage Performance and Capacity Reporting - Response Impacts (7 of 10)

SAN or storage array performance problems can be identified at the host or backend storage environment.

The diagram below shows a typical performance impact in the more complex environment.

With SAN attached storage you can share storage across multiple servers, one of the downsides of this is that you can have storage response impact across multiple servers too.

Performance Capacity – Host Metrics

It's important that you understand the limitations of certain host metrics.

A selection of host metrics are shown below:

•        Measured response is the best metric for identifying trouble.

•        Host utilization only shows busy time, it doesn’t give capacity for SAN.

•        Physical IOPs is an important measure of throughput, all disks have their limitation.

•        Queue Length is a good indicator that a limitation has been reached somewhere.

Performance Capacity – Host Metrics

Metrics like host utilization can indicate impactful events, but ample capacity might still be available.

The high utilization can be seen generating large amounts of I/O in the chart below.

Queue lengths indicate that it may not currently be impacting response, but headroom is unknown. Response time is the key, as users will be impacted if it goes up.

On Wednesday I’ll be taking a look at array architecture.

Dale Feiste

Principal Consultant

Key Metrics for Effective Storage Performance and Capacity Reporting - Two Distinct Aspects of Storage Capacity (2 of 10)

Today let’s take a look at the two distinct aspects of data storage.

Data can come from all different directions to the disk.

Disk occupancy

Disks used to be very expensive but now the costs have come down dramatically and this cost factor has accelerated the growth of storage.

You may have too little storage resulting in out of disk space problems but conversely you may have storage over-allocated. A lot of times people put excessive storage space out there to ensure that they never run out and don’t pay attention to how much they really need and what their growth really is going to be.

Below is a typical service center queuing diagram

Disk Performance Capacity

Response, IOPs

In many cases the requests are being sent out by an application or applications. There is a finite limitation on the requests per second that can be satisfied and then a queue begins to form. The queuing theory comes in to play where you have limitations on the throughput of your I/O and at some point this will have a response impact. The response impact transfers up through the application to the user and results in a slow response time, a performance problem.

On Friday I’ll be looking at space utilization, in the meantime why not sign up to our Community and get access to our great resources, free white papers, on-demand webinars and more.http://www.metron-athene.com/_resources/index.html

Dale Feiste

Principal Consultant

Backend Metrics - Key Metrics for Effective Storage Performance and Capacity Reporting(9 of 10)

Below are some back end metrics available on the back end storage array

These are typical performance metrics showing throughput and response times, the type of thing you need to report on regularly so that you can be on top of performance before incidents start being generated. 

Performance Capacity – Array Metrics

The key metrics that you need to get a handle on at volume level are throughput, response and latency.

Below an example of NetApp metrics at volume level

Below an example of metrics within EMC at the volume level

The read/write ratio can give you an idea of what your work profile looks like.

Performance Capacity – Component Breakdown

The example below, using athene, shows a component breakdown for the server.

It’s essential to know whether you have any queuing going on (shown in yellow above) as if queuing is happening you are exceeding the devices throughput rate.

In the final part of my blog series on Monday I’ll take a look at workload profiles, scorecards and dashboards.

Dale Feiste

Principal Consultant

Performance Capacity – Response Impacts - Key Metrics for Effective Storage Performance and Capacity Reporting (7 of 10)

SAN or storage array performance problems can be identified at the host or backend storage environment.

The diagram below shows a typical performance impact in the more complex environment.

 

With SAN attached storage you can share storage across multiple servers, one of the downsides of this is that you can have storage response impact across multiple servers too.

Performance Capacity – Host Metrics

It is important that you understand the limitations of certain host metrics.

A selection of host metrics are shown below:

•       Measured response is the best metric for identifying trouble.

•       Host utilization only shows busy time, it doesn’t give capacity for SAN.

•       Physical IOPs is an important measure of throughput, all disks have their limitation.

•       Queue Length is a good indicator that a limitation has been reached somewhere.

Performance Capacity – Host Metrics

Metrics like host utilization can indicate impactful events, but ample capacity might still be available.

 

The high utilization can be seen generating large amounts of I/O in the chart below.

Queue lengths indicate that it may not currently be impacting response, but headroom is unknown. Response time is the key, as users will be impacted if it goes up.

Next time I’ll look at array architecture. 
If you missed our recent webinar on Storage performance sign up for our Community and download or listen for free 
http://metron-athene.com/_downloads/on-demand-webinars/index_2.asp

Dale Feiste

Principal Consultant