Restoring Data Storage Predictability

Transcription

1 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 1

2 Agenda The Predictability Challenge Storage Background Storage Capacity Management Storage Performance Management 2

3 The Predictability Challenge What risky conditions exist right now across our entire environment? (rated metrics & exception charts for space, performance, configuration issues) What related metrics are relevant to the context of this issue? (side-by-side mini-charts of related metrics that are clickable) Where do go next to see root causes? (intelligent drill downs) What help is there to create a solutions? (built-in recommendations) 3

4 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 4

5 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 5

6 Predictability through ITOA as a Service Spend time on proactive storage management Not reactive fire fighting Not on maintaining the analytics infrastructure Allows for quick, easy Proof of Concept (POC) IntelliMagic

7 Storage Background 7

8 What is Data Storage? Remote Storage Primary Storage This Photo by Unknown Author is licensed under CC BY-SA This Photo by Unknown Author is licensed under CC BY-NC 8

9 Data Center Storage Architectures and Industry Adoption 9

10 10

11 State of Industry State of Technology 11

12 State of Industry State of Technology 12

13 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 3 Nearline/NAS Medium to slow, but predicable response times 4 Tape/VTS Archive Good Any IOPS average/throughput Not latency sensitive, think batch/archive 5 Cloud Slow and unpredictable Average Any IOPS low intensity Medium growth Any None IOPS low intensity/occasional high throughput High throughput is okay High growth High growth High High Moderate Moderate 13

14 Storage Capacity Management 14

15 Storage Capacity Management Methodology Collect Identify Important Metrics Report Make Recommendations Calculate Growth Forecast Requirements 15

16 Technology Enhanced Storage Capacity Management Methodology Collect Identify Important Metrics Report Make Recommendations Calculate Growth Forecast Requirements Automated Analysis 16

17 Storage Capacity Measurement (Local) 17

18 Storage Capacity Measurement (NAS) 18

19 Storage Capacity Measurement (SAN) 19

20 Storage Capacity Measurement (Hyper-converged) 20

21 Storage Capacity Measurement (Public Cloud) 21

22 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 22

23 Example of Burnout 23

24 Burn out Tabular View for Multiple Systems 24

25 Track Capacity By Application 25

26 Storage Performance Management 26

27 Storage Performance Management Methodology Load Collect Report Make Recommendations Assess Correlate 27

28 Technology Enhanced Storage Performance Management Methodology Prepare Collect Enrich Recommend Assess Automated Analysis Correlate Rate Visualize 28

29 Storage Performance Measurements: Collect 29

30 Prepare: Validate, Normalize and Categorize 1. Validate 2. Normalize: EMC vs HDS 3. Categorize 30

31 Enrich: Add Meaning 31

32 Enrich: Add Meaning 32

33 Enrich: Add Meaning 33

34 Assess: Define the Criteria 1. Hardware Specific Storage System Throughput 2. Workload Dependent Storage System Response Time 34

35 Assess: Define the Criteria 35

36 nn ( ii=1 rr ii )/n Where Rate: Apply The Assessment Criteria 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 36

37 Visualize: Visualize the Rating How does color relate to the rating displayed? 0-.1 is Green >.1-.3 is Yellow > 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. 37

38 Correlate Configuration 38

39 Apply Rating to Correlation? Port Issues 39

40 Application Views 40

41 How Can You Restore Data Storage Predictability? Challenges Automated Analysis Automatic Correlation Benefits Accurately and Quickly Identify Risks Highlight potential affected paths Application Performance Understand health of applications Capacity Forecasting Plan for demand 41

42 IntelliMagic Vision Architecture 42

43 IntelliMagic Vision for SAN Logical Architecture 43

44 IntelliMagic Vision as a Service Architecture 44

45 Thank you For more information, please visit Contact us with any questions or feedback: 45

46 Save the Date!