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Restoring Data Storage Predictability
Thoughts and Approaches on Managing Storage Performance and Capacity in 2017
Brent Phillips – Managing Director, Americas Brett Allison – Director of Technical Services
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Agenda
• The Predictability Challenge
• Storage Background
• Storage Capacity Management
• Storage Performance Management
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The Predictability Challenge
• What risky conditions exist right now across our entire environment? (rated metrics & exception charts for space, performance, configuration issues)
• Where do go next to see root causes? (intelligent drill downs)
• What related metrics are relevant to the context of this issue? (side-by-side mini-charts of related metrics that are clickable)
• What help is there to create a solutions? (built-in recommendations)
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Predictability Requires Better Analytics • Lots of disparate data from:
‒ Hosts ‒ SAN Switches ‒ Storage Arrays
• Need to automatically: ‒ Normalize the data ‒ Enrich, additional calculations ‒ Correlate, interrelate ‒ Evaluate, good or bad? ‒ Easily navigate through it
IT Operations Analytics (ITOA)
"The use of mathematical algorithms and other innovations to extract
meaningful information from the sea of raw data collected
by management and monitoring technologies.”
Forrestor Research
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Predictability Requires Better Analytics
• “..and other innovations…” ‒ Most useful ITOA innovation for storage is applying a
storage-specific type of artificial intelligence (AI).
• “Artificial intelligence is the science of making machines do things that would require intelligence if done by men” - Marvin Minsky 1968
• What could be done that there is no time to do?
• This is an example of why Applied AI is the #1 strategic technology trend according to Gartner
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• Spend time on proactive storage management ‒ Not reactive fire fighting ‒ Not on maintaining the analytics infrastructure
• Allows for quick, easy Proof of Concept (POC)
Predictability through ITOA as a Service
© IntelliMagic 2016
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Storage Background
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What is Data Storage?
This Photo by Unknown Author is licensed under CC BY-SA This Photo by Unknown Author is licensed under CC BY-NC
Primary Storage Remote Storage
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Data Center Storage Architectures and Industry Adoption
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State of Industry
State of Technology
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State of Industry
State of Technology
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Performance Management Characteristics Tier Purpose Performance External
Dependencies * I/O Profile Ideal
Capacity Growth
Availability
0 Flash Extremely Fast Any IOPS intensive Average High (if in enterprise storage array)
1 Enterprise Hybrid
Fast Any IOPS intensive Average High
2 Mid-range Spinning
Good Any IOPS average/Throughput
Average High
3 Nearline/NAS Medium to slow, but predicable response times
Any IOPS low intensity Medium growth
High
4 Tape/VTS Archive
Not latency sensitive, think batch/archive
Any IOPS low intensity/Occasional high throughput
High growth
Moderate
5 Cloud Slow and unpredictable
None High throughput is okay
High growth
Moderate
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Storage Capacity Management
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Storage Capacity Management Methodology
Collect
Report
Calculate Growth
Forecast Requirements
Make Recommendations
Identify Important Metrics
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Technology Enhanced Storage Capacity Management Methodology
Collect
Report
Calculate Growth
Forecast Requirements
Make Recommendations
Identify Important Metrics
Automated Analysis
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Storage Capacity Measurement (Local)
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Storage Capacity Measurement (NAS)
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Storage Capacity Measurement (SAN)
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Storage Capacity Measurement (Hyper-converged)
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Storage Capacity Measurement (Public Cloud)
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Common Storage Capacity Forecasting Techniques
• HisS: Historical Swag or Order about the same as last year
• LRA: Linear Regression Analysis: Apply linear regression analysis to your usable capacity trend from the previous year(s). Continue growth line for some time in future.
• ABRBO: Burn rate/Burn out: Calculate average burn rate per day/month/etc. Divide capacity left by burn rate to calculate days until burn out.
• WARP: Wait and Reach out to vendor in Panic
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Example of Burnout
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Burn out Tabular View for Multiple Systems
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Track Capacity By Application
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Storage Performance Management
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Storage Performance Management Methodology
Load
Report
Assess
Correlate
Make Recommendations
Collect
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Technology Enhanced Storage Performance Management Methodology
Prepare
Enrich
Assess
Rate
Visualize
Correlate
Recommend
Collect
Automated Analysis
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Storage Performance Measurements: Collect
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Prepare: Validate, Normalize and Categorize
1. Validate 2. Normalize: EMC vs HDS
3. Categorize
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Enrich: Add Meaning
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Enrich: Add Meaning
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Enrich: Add Meaning
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Assess: Define the Criteria
1. Hardware Specific Storage System Throughput
2. Workload Dependent Storage System Response Time
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Assess: Define the Criteria
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Rate: Apply The Assessment Criteria
• (∑ 𝑟𝑟𝑖𝑖𝑛𝑛𝑖𝑖=1 )/n
‒ Where • r = rating at interval i • n = number of intervals
‒ Rating is always either • 0 = Value is less than warning • 1 = Value is greater than or equal to warning or less than
performance exception • 3 = Value is greater than or equal to performance exception
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Visualize: Visualize the Rating
• How does color relate to the rating displayed? ‒ 0-.1 is Green ‒ >.1-.3 is Yellow ‒ >.3-3.0 is Red
• So out of 96 intervals: ‒ we need no more than 3 red or 9 yellow intervals to rate
green. ‒ Less than 28 yellow or 9 red intervals make the chart rated
yellow. ‒ Otherwise, the chart is rated red.
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Correlate Configuration
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Apply Rating to Correlation?
Port Issues
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Application Views
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Automated Analysis
Automatic Correlation
Application Performance
Capacity Forecasting
How Can You Restore Data Storage Predictability?
Accurately and Quickly Identify Risks
Highlight potential affected paths
Understand health of applications
Plan for demand
Challenges Benefits
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IntelliMagic Vision Architecture
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IntelliMagic Vision for SAN Logical Architecture
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IntelliMagic Vision as a Service Architecture
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Thank you
For more information, please visit
www.intellimagic.com
Contact us with any questions or feedback:
Email: info@intellimagic.com
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