Your SlideShare is downloading. ×
0
Flash 1st – A powerful data centre                                                         optimization and consolidation ...
2009: More Apps On Virtual Infrastructure                17,500,000                15,000,000                             ...
Virtualization Changes Everything…SLA optimization has shifted from Point-in-Time to Real-Time                    MANUAL P...
Information Tipping Point AheadThis decade will be nothing like the last  140,000  120,000                                ...
Most Data Will be Stone ColdWhile we increasingly can’t erase data, we can store it better                                ...
The CPU to HDD Performance GapCPU improves 100 times every decade – disk speed hasn’t        Moore’s Law will continue to ...
Clicker QuestionHave you faced the following challenges.A. Due to performance requirements your storage solution   was bui...
Anatomy of an Enterprise FLASH DriveDesigned for reliability, data integrity and performance                              ...
9          Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved.       17-18 November 2011. Grand Hya...
Comparing Associated CostsWhich technology is the most efficient?                Capacity Acquisition Cost                ...
How?                                                            11      Cloud Meets Big Data© Copyright 2011 EMC Corporati...
The FLASH 1st Data StrategyHot data on fast FLASH SSDs—cold data on dense disks                          “Hot”            ...
Clicker QuestionHave you had discussion with your solution suppliers aroundthe mix of flash and spinning drives• Yes – we ...
How Much FLASH?Used dynamically with Automated tiering                       Size of FLASH FIFO                • Older dat...
How Much FLASH?3 fundamental business questions                                                         • How much data ar...
Calculating Needed FlashNet new is a function of amount of “starting data” and “growth”                                   ...
Clicker QuestionWhat has been your biggest hindrance to flash 1 st strategy.A. We feel flash is too expensiveB. We preferr...
Workload Skew Defines Configuration and benefitsTake Advantage of Workload Skew        Heavy Skew                         ...
Automatic Data Optimization – FAST VPThe benefit of FLASH without the cost                                                ...
FAST Cache                                                  • Fully automatic application accelerator                     ...
Disk Utilization   Application was experiencing higher response time-Disk Utilization touching 80%                        ...
Lets change the way we procure IT                                                         Faster Response Times           ...
THANK YOU                                                         Thank You                                               ...
Upcoming SlideShare
Loading in...5
×

Track 1, Session 2, Flash by Amit Sharma

381

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
381
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
11
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • The number of virtual machines has overtaken the number of physical server. Gap increasing further. Virtual 1st policy.
  • Transcript of "Track 1, Session 2, Flash by Amit Sharma"

    1. 1. Flash 1st – A powerful data centre optimization and consolidation approach 1 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    2. 2. 2009: More Apps On Virtual Infrastructure 17,500,000 15,000,000 The Tipping Point Virtual Machines 12,500,000 Physical Hosts 10,000,000 7,500,000 5,000,000 2,500,000 2005 2006 2007 2008 2009 2010 2011 2012 2013Source: IDC 2 Cloud Meets Big Data © Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    3. 3. Virtualization Changes Everything…SLA optimization has shifted from Point-in-Time to Real-Time MANUAL PROCESS 100% AUTOMATED Oracle Exchange SQL server server server Oracle Exchange SQL OracleI ExchangeI SQLI Automated Tiering SSD SAS NL-SAS Static Association of Dynamic, Self-optimizing Disk Groups to Applications Storage Pools based on best guess based on actual data activity 3 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    4. 4. Information Tipping Point AheadThis decade will be nothing like the last 140,000 120,000 Data 100X 100,000 A 10TB data center in 2001 80,000 growing at 60% YoY will be a 120 Petabyte data center by 60,000 MORE DATA 2021, but IT budgets remain flat EACH DECADE Automation 40,000 20,000 Budget 0 2000 2003 2006 2009 2012 2015 2018 2021 4 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    5. 5. Most Data Will be Stone ColdWhile we increasingly can’t erase data, we can store it better 4,123 100 X 37.5 TB TB 2011 2021 Hot Warm Cold Evolution of 50 TBs in 10 Years 5 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    6. 6. The CPU to HDD Performance GapCPU improves 100 times every decade – disk speed hasn’t Moore’s Law will continue to improve CPU performance while disk drive performance will remain flat. As a result, applications will suffer more and more unless we rapidly move to FLASH 10,000 times FLASH improved 100 times improved 2000 2010 2020 6 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    7. 7. Clicker QuestionHave you faced the following challenges.A. Due to performance requirements your storage solution was built of a huge number of spinning drives whereas your capacity requirement was much lower?B. You still prefer 15K spindles and have your arrays totally filled up?C. You were forced to do incremental purchases over the life span of your infrastructure to meet performance SLA’s.? 7 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    8. 8. Anatomy of an Enterprise FLASH DriveDesigned for reliability, data integrity and performance Controller SLC NAND FLASH SAS or SATA ports DRAM End to End CRC 8 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    9. 9. 9 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    10. 10. Comparing Associated CostsWhich technology is the most efficient? Capacity Acquisition Cost Capacity Power Cost $- $10 $20 $30 $40 0 5 10 15 20 25 30 15K HDD 15K HDD 7200 HDD 7200 HDD FLASH FLASH FLASH 7200 HDD 15K HDD FLASH 7200 HDD 15K HDD $/GB $34.80 $0.43 $1.63 mWatt/GB 25 6 28 Transaction Acquisition Cost Transaction Power Cost $- $2 $4 $6 $8 $10 $12 0.0 50.0 100.0 150.0 15K HDD 15K HDD 7200 HDD 7200 HDD FLASH FLASH FLASH 7200 HDD 15K HDD FLASH 7200 HDD 15K HDD $/IOPS $1.99 $9.56 $5.44 mWatt/IOPS 1.4 133.3 94.4 10 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    11. 11. How? 11 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    12. 12. The FLASH 1st Data StrategyHot data on fast FLASH SSDs—cold data on dense disks “Hot” high activity Highly active data is As data ages, activity stored on falls, triggering automatic FLASH SSDs for movement to high capacity fastest disk drives for lowest cost response time Data Activity FLASH High Cap. SSD HDD Movement Trigger “Cold” low activity Data Age (5 years) 12 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    13. 13. Clicker QuestionHave you had discussion with your solution suppliers aroundthe mix of flash and spinning drives• Yes – we had detailed discussions and some sizing methods were implemented• No – we had no discussions 13 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    14. 14. How Much FLASH?Used dynamically with Automated tiering Size of FLASH FIFO • Older data is constantly being replaced by new Amount of Data being highly active data created daily • The amount of FLASH required is determined by: – The amount of data created FLASH Capacity each day, and – The period of time it takes to cool Number of Days of High Data Activity 14 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    15. 15. How Much FLASH?3 fundamental business questions • How much data are you storing today? 50 TBs • How much is your data growing each year? 50% YoY • How long does your data stay hot? 60 days 15 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    16. 16. Calculating Needed FlashNet new is a function of amount of “starting data” and “growth” • 50% of 50TB is 25TB • The average amount of data 50% growth rate generated each day: 80 – 25 X 1024 GB / 365 = 70 GB per day 60 25 • FLASH Capacity: 40 – 60 days X 70 GB = 50 50 4,200 GB 20 0 • FLASH Percentage: Start Year 1 – FLASH Capacity/Total Capacity – 4,200 GB/(75 X 1024) GB X 100: 6% 16 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    17. 17. Clicker QuestionWhat has been your biggest hindrance to flash 1 st strategy.A. We feel flash is too expensiveB. We preferred continuing with spinning spindles as it was tried and testedC. We need help to ascertain our workloadsD. We feel with flash and advanced tiering there is complexity. 17 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    18. 18. Workload Skew Defines Configuration and benefitsTake Advantage of Workload Skew Heavy Skew Moderate Skew Low Skew Zero Skew 95% of I/O on 90% of I/O on 80% of I/O on 50% of I/O on 5% of data 10% of data 20% of data 50% of data Configuration A Configuration B Configuration C Configuration D 3% EFD 3% EFD 3% EFD 25% FC 15 K 43% FC 15 K 80% FC 15 K 97% SATA 72% SATA 54% SATA 20% SATA 30% More 40% More 20% More Same Performance Performance Performance Performance 80% Fewer Disks 60% Fewer Disks 50% Fewer Disks 17% Fewer Disks 20% Lower Costs 15% Lower Costs Same Cost 13% Lower Costs 18 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    19. 19. Automatic Data Optimization – FAST VPThe benefit of FLASH without the cost • Easy and Effective Implementations – Per-application Policies, modeled by professional tools – Works with everything, validated against real workloads Active Inactive 5% 95% Data FLASH Disk Data • Exclusion policies • SLA based Tiering – Promote/Demote policies SSD HDD • Granular Data Management – Minimum chunk of movement – Batch vs realtime , frequency of analysis • Advanced Controls – Time controls for performance and movement – 3 Tiers, dynamic, online 90% utilization 19 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    20. 20. FAST Cache • Fully automatic application accelerator – System-wide resource, immediate impact – No extensive pre-planning needed FAST Cache – Helps both Read and Write operations – Quick adjustment to changing usage patterns – Up to 2TB Flash Fibre • Turbo-charge performance improvement in Channel Oracle, Microsoft SQL Server, and VMware View environments Turbo-charge performance SATA Benefits Easy to manage and monitor 20 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    21. 21. Disk Utilization Application was experiencing higher response time-Disk Utilization touching 80% Before FAST Cache After FAST Cache • Application response time improved • Drive utilization reduced. • Response time increased • Additional Headroom 21 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    22. 22. Lets change the way we procure IT Faster Response Times Smaller Footprint Less Power Lower Cost 22 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    23. 23. THANK YOU Thank You 23 Cloud Meets Big Data© Copyright 2011 EMC Corporation. All rights reserved. 17-18 November 2011. Grand Hyatt - Mumbai
    1. A particular slide catching your eye?

      Clipping is a handy way to collect important slides you want to go back to later.

    ×