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Key Metrics for Effective Storage Performance and Capacity Reporting.

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Presentation on theme: "Key Metrics for Effective Storage Performance and Capacity Reporting."— Presentation transcript:

1 Key Metrics for Effective Storage Performance and Capacity Reporting

2 Abstract Doing capacity management for storage can be difficult with the many complex and varied technologies being used. Given all of the options available for data storage strategy, a clear understanding of the architecture is important in identifying performance and capacity concerns. A technician looking at metrics on a server is often seeing only the tip of a storage iceberg. Knowing which metrics are important will depend on your objectives and storage architecture, but response and space utilization will always be key to effectively managing storage.

3 Contents Storage Architecture Two distinct aspects of storage capacity Virtualization Key metrics from the host and backend storage view Reporting on what is most important

4 Space Capacity - History Growth can result in increasing cost and complexity

5 Two Distinct Aspects of Storage Capacity and Performance Storage Space Storage Throughput Response, IOPS

6 Space Capacity – Space Utilization What does storage Utilization mean in your environment? Factors include: RAID/DR, Raw/Configured, Host/SAN, Backups, Compression, Etc...

7 Space Capacity – Proactive Visibility Alarm on key metric trends instead of current threshold breaches to get in front of problems before they happen. Trending, forecasting, and exceptions.

8 Space Capacity – Trending Understand the limitations of linear regression when trending and forecasting data. Chart above has high correlation Chart below has low correlation

9 Space Capacity – Showing Different Viewpoints Business, Application, Host, Storage Array, Billing Tier

10 Space Capacity – Host Metrics Metrics are typically available at the file system, volume and logical disk views. Key metrics for space capacity from the host perspective are typically: Storage allocated to system (disks) Allocated but not configured (volumes) Space used or free (file systems)

11 Space Capacity – Array Metrics Storage arrays can have many space related metrics at different levels NetApp Aggregate Key metrics for space capacity from the array perspective depends on the technology and how it is being used. However, like the host view, total capacity and space available are key metrics: Storage installed in arrays (disks) Configured but not allocated (aggregates) Space used or free (volumes)

12 Virtual Environments and Clusters Thin provisioning Storage viewed at many levels Could be different tiers allocated to the same cluster Overhead at various points Managing storage in clustered and/or virtual environment can be challenging because it is shared among all hosts and virtual machines running on it. Image Source:

13 Storage Virtualization Can be a centralized source for collecting data Pooling physical storage from multiple sources into logical groupings Wide variety of techniques for virtualizing storage, be aware of the implications for data collection and reporting

14 Performance Capacity – Response Impacts SAN or storage array performance problems can be identified at the host or backend storage environment. Response is the key metric for performance evaluation Host I/O response Fabric or Network response Virtualization device response Array response High response is typically caused by insufficient throughput capacity

15 Performance Capacity – Host Metrics Understand the limitations of certain host metrics Measured response is the best metric for identifying trouble. Host utilization only shows busy time, it doesnt give capacity for SAN. Physical I/O rate is an important measure of throughput, all disks have their limitation. Queue Length is a good indicator that a limitation has been reached somewhere.

16 Performance Capacity – Host Metrics 100% host disk utilization can indicate high throughput, but ample backend capacity might still be available, as was the case here.

17 Performance Capacity – Host Metrics Queue lengths from the previous high utilization chart indicates that it may not currently be impacting response, but headroom is unknown.

18 Performance Capacity – Host Metrics I/O generated from the previous high utilization chart is shown here, where combined throughput peaks are very high.

19 Performance Capacity – Host Metrics Spikes in throughput typically correlate with queues and response for simple disk configurations, as seen in the chart, but most disk configurations are not simple anymore, which means these metrics often do not correlate.

20 Performance Capacity – Array Architecture Front End Processors Shared Cache Back End Processors Disk Storage

21 Performance Capacity – Array Metrics Front end processors are typically the first to bottleneck. This chart shows acceptable utilization.

22 Performance Capacity – Array Metrics Find arrays doing the most work with throughput metrics.

23 Performance Capacity – Array Metrics Aggregating and trending key metrics can be useful as shown here.

24 Performance Capacity – Array Metrics Knowing what is generating the IOPS can also be important

25 Performance Capacity – Storage Virtualization Metrics Key metrics are also available from virtualization devices. This chart shows the top 10 IBM SVC volumes for throughput.

26 Performance Capacity – Storage Virtualization Metrics This is another example of aggregating and trending, although this particular SVC data sample is not a good real world example.

27 Performance Capacity – Storage Virtualization Metrics Storage devices can have many performance metrics at different levels Key metric for performance evaluation is response. Other metrics are important too, but are typically used to avoid or troubleshoot high response times.

28 Performance Capacity – Array Metrics NetApp EMC Response

29 Performance Capacity – Component Breakdown Service time versus response time – different metrics The bar chart shows service times as blue and green, with queue time represented as red and yellow. Response is the combination of service and queue time. IO Response

30 Performance Capacity – Workload Profiles Application type is important in estimating performance risk

31 Performance Capacity – Scorecards and Exceptions

32 Performance Capacity – Dashboards At a glance view of important metrics for critical resources

33 Storage Key Metrics – Conclusions Knowledge of your storage architecture is critical Understand both storage space and throughput Consider all factors that affect storage space utilization Be aware of virtualization and clustering complexities Know key metrics and their limitations Start with key report types and areas that are most important

34 Key Metrics for Effective Storage Performance and Capacity Reporting Thank you for attending The End

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