Key metrics for effective storage performance and capacity reporting

Key metrics for effective storage performance and capacity reporting2

Key Metrics for Effective Storage Performance and Capacity ReportingObjectives

This white paper will cover the key metrics in storage that you can use to get a handle on your storage capacity.

• Storage Architecture – basic concepts

• Two distinct aspects of storage capacity

• Virtualization

• Key metrics from the host and backend storage view

• Reporting on what is most important

Space Capacity – History

Storage increasing in complexity from left to right on the diagram below.

Large environments have gone from megabytes to petabytes in terms of storage, growth can result in increasing cost and complexity.

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Two Distinct Aspects of Storage Capacity

Data can come from all different directions to the disk.

Disc 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 performance 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.

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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.

Space Capacity – Space Utilization

What does storage ‘Utilization’mean in your environment?

Utilization can be a variable definition and there are many factors to take in to account, these include RAID/DR, Raw/Configured, Host/SAN, Backups, Compression, Etc...

The term utilization can depend on whether you are including any of these factors and it is useful to know exactly what you wish to include and report on when determining whether you have under or over-utilized storage capacity.

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Occupancy – Visibility

Once you have defined what you wish to include in your reports you can start collecting the data.

The chart below illustrates space used on a file system and is a regular trend chart with a threshold, as you can see moving out in to the future it is going to exceed the threshold. You can use trending to report on a number of metrics but when an application is going to run out of space it is going to be at this level.

It’s advisable to be pro-active with trending to ensure that you can deal with any problems before they turn in to real performance problems.

Technical solutions can then be implemented to optimize storage space management, including databases.

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Space Capacity – Trending

One thing to keep in mind for trending is to understand the limitations of linear regression when trending and forecasting data.

I’ve used the graphs below as an example of this.

In the second graph you can see what will happen eventually when that bottoms out or someone goes in and allocates more storage or frees more storage up – it skews the trend line.

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Space Capacity – Different Viewpoints

We’ve talked about different viewpoints when looking at your data, reports, trending and now I’m going to look at how useful it is to look at things in Groups.

You can group by Business, Application, Host, Storage Array, Billing Tier and what that really boils down to is providing more of a business or application view.

Below you can see this has been grouped to provide a commercial/business and a technical view. Application owners can go in and see how much storage they are consuming, particularly useful if you also include billing information.

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Space Capacity – Host Metrics

Moving on to the metrics, for occupancy the key metric is utilization. How much storage are we using and how much is available?Below are some host metrics that are typically available, these metrics are available at the file system, volume, or logical disk levels.

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Space Capacity – Array Metrics

The illustration below shows an example of occupancy metrics from the array perspective. This an example of a Netapp filer with an aggregate (down at the aggregate level)

A lot of these Storage arrays, from the different vendors, have different ways to carve up the storage. You can set up as storage groups or as in this example, using Netapp, aggregates which can have many occupancy metrics at different levels.

Some of the Netapp occupancy levels here are not available on the host in general.

I’ll pick out a few of the metrics:

De-dupe – If this is turned on you can finds out how much space you’re saving

Total Committed space – A lot of vendors now offer thin provisioning where storage can be over-committed so it looks as though there is more storage than is really available, this allows you to see how over-committed you really are.

athene, our capacity management solution, can bring in metrics from any time series data source so can allow storage metrics to be brought in to the capacity management process.

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Virtual Environments and Clusters

Managing storage in clustered and/or virtual environments can be challenging because it is shared among all hosts and virtual machines running on it.

Below is an example of a VMware cluster, just a simple 3 node cluster going to some shared storage.

Features that are available

• Thin provisioning

• Storage can be viewed at many levels.

• Could be different tiers allocated to the same cluster

• Overhead at various points

How do you know what the overhead is? Aggregate all the storage up, compare it to what is allocated and calculate the difference, this will give you a good indication of your overhead.

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Storage Virtualization

There are advantages to the layered system

• it allows a caching layer so that you may not have to go all the way to the backend to satisfy an I/O request

• there are a lot of administrator features regarding allocation and replication

Pooling physical storage from multiple sources into logical groupings is useful

• Can be a centralized source for collecting data

• If using as a data source beware of double counting with backend

There are a wide variety of techniques for virtualizing storage, be aware of the implications for data collection and reporting.

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Performance Capacity – Response Impacts

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

The diagram below shows the storage evolving in to a 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.

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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.

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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.

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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.

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Performance Capacity – Array Architecture

This is an example of an enterprise type array comprising of

• Front End Processors

• Shared Cache

• Back End Processors

• Disk Storage

A lot of time these disks can be striped across the entire array, a very large number of spindles tied together to provide a very large resource.

Quite often on these large arrays bottlenecks will occur on the front end processor, requests coming in will queue up there.

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Performance Capacity – Array Metrics

As mentioned front end processors are typically the first to bottleneck, below is an example showing just one day.

This is ideal information for trending, if you picked up these processors over a period of time you could do a trend going forward and figure out when and where bottlenecks are likely to occur.

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Performance Capacity – Backend Metrics

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.

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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

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.

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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.

Performance Capacity – Workload Profiles

As mentioned previously the Read/Write metrics can help you to get a handle on your workload profiles.

Application type is important in estimating performance risk, for instance, something like Exchange is a heavy I/O user.

I’ve also seen examples where virtual workstations were being installed and resulted in a large I/O hit that could have impacted other applications sharing storage.

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Performance Capacity

ScorecardsThis is an example of a score card, where you can have a large amount of information condensed in to one easy to view dashboard.

DashboardsAn example below is how you can set up a dashboard and bring key trending and standard reports to you all in one place.

Trending, forecasting, and exceptions with athene®

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Storage Key Metrics – Summary

• Knowledge of your storage architecture is critical, you may need to talk to a separate storage team to get this information

• Define storage occupancy versus performance

• Discuss space utilization and define

• Review virtualization and clustering complexities

• Explore key metrics and their limitations

• Identify key report types and areas that are most important, start with the most critical

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