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Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer
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Rio Info 2009 - Optimizing IT Costs using Virtualization, Green and Cloud Computing - David Royer

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  • 1. Optimizing IT Costs using Virtualization, Green and PRESENTATION TITLE GOES HERE Cloud Computing David Royer SNIA Brasil, Chairman Rio Info 2009 Rio de Janeiro, Brazil
  • 2. SNIA At A Glance Voice of the storage industry representing approximately $50-60B in worldwide revenue for hardware and software Founded in 1997 as a non-profit trade association Worldwide headquarters in San Francisco USA Global presence in A/NZ, Canada, China, EMEA, India, Japan and South-Asia Technology Center activities in Colorado, Beijing, Tokyo, and Bangalore Focus on education, conferences, specifications / standards, software, industry alliances, best practices, plugfests, and conformance testing for SNIA specifications Co-owner of Storage Networking World (SNW) conference with Computerworld/IDG Enterprise a collaborative environment and serve as global contributors toward the advancement of standards, education, and innovation in the storage and information management industry
  • 3. Storage Outlook and Growth
  • 4. Worldwide Disk Storage Systems and Branded Tape Storage Segment Factory Revenue Growth YoY Growth by Segment 30.00% 20.00% 10.00% 0.00% -10.00% Q1 Q2 Q3 Q4 -20.00% 08 08 08 08 20 20 20 20 -30.00% -40.00% -50.00% -60.00% -70.00% Tape - Entry Level Tape - Midrange Tape - High End Int Disk - Entry Int Disk - Midrange Ext Disk - Entry Ext Disk - Midrange Ext Disk - High End • Entry level and midrange external DSS are the only segments showing flat/positive YoY growth in 4Q 2008. This can be attributed to: customers deferring purchase of larger, more expensive storage systems in favor of lower cost, more modular systems and; the emergence of technologies, such as iSCSI, that offer enterprise level features yet at a lower price point than traditional FC SAN systems
  • 5. Storage Hardware 2009 Outlook Tape will continue to decline as disk-based archival and back-up technologies emerge Internal storage is closely tied to the server market, which is expected to be weaker in the coming quarters than the external disk market External disk storage systems market will feel further the impact of the economic crisis. Weakness seen in higher end systems, specifically mainframes and FC SAN. Healthier segments include: iSCSI SAN – specifically in the upper entry level and midrange market Verticals such as Healthcare, Video Surveillance, and Government Midrange product offerings: as customers fulfilling their enterprise storage needs with midrange products Enterprise VTL: Will augment midrange and enterprise tape drives, especially in tape libraries and automation Source IDC Doc # 218274
  • 6. Storage Software Growth – Average 7% Data Protection, growth rate through 2013, 6.2% Archiving Software, growth rate through 2013, 10.4% Storage Device Management Software, growth rate through 2013, 2.8% Storage Management Software, growth rate through 2013, 5.6% Storage Infrastructure, growth rate through 2013, 5.9% Storage Replication, growth rate through 2013, 7.6% File System, growth rate through 2013, 7.1% Source IDC Doc # 217529
  • 7. E-Discovery Growth Combination of software: Storage infrastructure, e-discovery, collaboration, ECM, data management, and security Hardware Storage spending growth was underpinned by data volume and requirements to store, manage, index, archive, and preserve data Servers Source IDC Doc # 218259
  • 8. Focus on a Few Industry Storage Trends Green IT Cloud Computing Virtualization
  • 9. Abstract Best Practices in Managing Virtualized Environments Today, data center environments are increasingly complex with virtualization at all layers of the IT stack, including network, server, SAN and storage. IT professionals are often challenged in diagnosing application performance issues, optimizing infrastructure resource utilization, and planning for future changes. The best practices for managing complex data center environments include cross domain management orientation, watching the infrastructure response time for cross-domain performance, looking for application contention and contention-based latency in the storage layer, best fit analysis of workloads to storage resources, and working toward infrastructure performance SLAs. Key requirements for this new breed of management software include agent-less discovery and SMI-S support. 9
  • 10. Virtualization is Everywhere Tremendous Benefits Pooling of resources Rapidly deploy new App Servers Web Servers Security applications Client Network NETWORK Increase resource utilization Server Virtualization Over-subscribe resources Lower acquisition cost and Storage Network SAN SAN TCO Traditional system Array Virtualization management practices may no longer work 10
  • 11. What’s “Real” about Virtualization? Like the Emperor‟s new (virtualized) clothes – A logical interface presenting a normalized “resource” that isn‟t “all there” Built over physical and other virtual layers that do not look at all like the presented logical resource We will discuss two major IT virtualization initiatives Storage Virtualization Server Virtualization (and the combination of the two!) Check out SNIA Tutorial: Virtualization 1- What, Why, Where, and How 11
  • 12. Virtualization Pools Resources Physical Infrastructure Model Virtual Infrastructure Model CLIENT NETWORK CLIENT NETWORK Server Pool SAN SAN STORAGE NETWORLK Storage Pool Tier 1 Tier 2 Archive 12
  • 13. Managing Virtualized Environments Managing through Virtualization is Challenging Diagnosing Performance Problems Optimizing Resource Utilization Planning for Future Changes Virtualization Feature “New” Admin Challenge Clients Reserve and Share Resource Performance still Resource Capacity Degrades Non-linearly with Load Dynamic Infrastructure Finding Transitional bottlenecks Increased Resource Utilization Optimal Resource Deployment Easy to provision new VMs Predicting if the next VM fits 13
  • 14. The Bottom Line… Applications share resources Poor performance is caused by: Hard-to-find I/O bottlenecks and resource contention Mis-alignment between layers of virtualization Under-provisioning shared resources Over-provisioning of shared resources as insurance negates ROI Inhibitors to success Virtualized data center complexity Lack of cross-domain management Lack of cross-domain communication 14
  • 15. Best Practices in Managing Virtualized Environments Solving Old Problems in a New Environment Recommended Best Practices - 1. Cross Domain Analysis and Shared Resource Contention 2. Adopt an Application View of Performance 3. Use Automation Wisely 4. “Effective Capacity” Management 5. Model-based Optimization and Planning 15
  • 16. 1. Cross Domain Analysis Virtualization Management is “Cross-Domain” - Create a Cross-Domain Baseline (discover and collect) Mapping from multiple layers (app, server, storage, physical & virtual) Aim for agent-less and “on-line” Standards like SMI-S are essential for heterogeneous environments Check Configuration First Don‟t optimize or “plan a baseline” from a poorly configured system Checklist vendor configuration best practices Newer technologies (Thin-wide arrays, 10 GbE networks, SSDs) move performance bottlenecks elsewhere. SNIA Tutorial: Check out Solving Business-Oriented Goals with SMI-S 16
  • 17. I/O Paths Through Virtualization Applications and Servers Virtual Server Hosts Virtual Storage Storage Arrays 17
  • 18. Find Shared Resource Contention Stepping Through a Virtual Looking Glass - Need to Map through Virtualization Layers Map relationships at every level Exponential problem of server virtualization over storage virtualization Sum up the loads from every client that shares each resource Quantify Application Contention due to Sharing Calculate performance impact back to each application Root cause is mostly figuring out What’s Changed when Capacity runs out If Load changed, was it aberrant behavior or growth? If Configuration changed, does it violate policy or show thrashing? If Contention arose, who is new to the pool? 18
  • 19. Application Contention Cross Domain visibility is naturally “foggy” Domain specific management has limited view Virtualization makes it harder Management requires end-to-end picture 19
  • 20. Cross-Domain: Navigating the Virtualized Environment A common map Need a map through helps different domain all the indirection admins communicate Long data path from application to array… Sharing can be dynamic – maps must be too 20
  • 21. 2. Adopt Application View of Performance The Customer is Always Right – Application Infrastructure Performance How long do it take an I/O to complete from the application point of view (Response Time) Some applications ($$$) are more loved than others Manage to this “Service” Performance Element utilizations are interesting, but service performance is the goal Look for Abnormal “Service” Behavior Not just default rule-of-thumb thresholds on utilizations 21
  • 22. Service Layer Metrics Customer Resource 40 35 30 25 Response 20 Time ( sec ) Optimal 15 Throughput Throughput @ 10 Service Level Agreement Response Time 5 Maximum Throughput 0 0 200 400 600 800 1000 1200 1400 Throughput ( transactions / sec ) 22
  • 23. Look for Abnormal Behavior Check for Abnormal Behavior Acceptable Variance Calculate baseline A statistical analysis of variance of performance over time Compare data to baseline Shared Resources tend to average out peaks that will show in dedicated resources Helps Justify Virtualization 23
  • 24. 4. “Effective Capacity” Management Capacity Management Isn’t Just “Enough GBs” Storage has both space and time constraints (server folk have it easy!) Manage to the total “Effective Capacity” Maximum utilization that gives good performance Not to total actual utilization (aka “saturation”) Build in Automation for Scalability Virtualized environments tend to sprawl And they can change dynamically Check out SNIA Tutorial: Storage Virtualization II – Effective Use of Virtualization 24
  • 25. Effective Capacity = Optimal Usage 40 35 30 25 Response 20 Time ( sec ) Optimal 15 Throughput 10 Service Level Agreement 5 Maximum Throughput 0 0 200 400 600 800 1000 1200 1400 Throughput ( transactions / sec ) 25
  • 26. 4. Use Automation Wisely Build in Automation for Scalability Virtualized environments tend to sprawl And they can change dynamically Almost everything can be automated Event Monitoring Performance collection and reporting Analysis of Performance and Configuration correlation of events with performance, first and second order analysis Provisioning, Reconfiguration and Migration Don‟t forget to leave an audit trail Feedback loop Check out SNIA Tutorial: Storage Virtualization II – What where the effects of the change? Effective Use of Virtualization 26
  • 27. 5. Model based Optimization and Planning Moving Towards a Real-Time Datacenter - Constantly Increase Operational Efficiency Most working infrastructure is sub-optimized Dedicated resources “If it ain‟t broke, don‟t fix it” attitudes (or capabilities) However, when everything is shared, everyone goes down together… Real-er Time Capacity Planning Utilizations are related to Response Time through Queuing Theory Need to predict performance degradation under future application load changes Need to predict performance improvements from possible architectural/technology changes Planning and tuning will go from large cyclical events to smaller, more dynamic perturbations 27
  • 28. Queuing Theory to The Rescue… Queuing Models create Response Time curves Based on established mathematics (Buzen, et.al – see www.cmg.org ) Useful analytically (historically) as well as predictively For a simple example think of a check-out line at the grocery store Complex Queuing Network Models can represent nested and virtualized IT domains Advanced cross-domain solutions model IT virtualization 28
  • 29. Best Practices in Managing Virtualized Environments In Summary - 1. Cross Domain Analysis and Shared Resource Contention Virtualization is about sharing across IT domains, and that‟s often the problem 2. Adopt an Application View of Performance Manage to customer service levels 3. Use Automation Wisely Doing more with less time and fewer errors 4. “Effective Capacity” Management Shared resources still obey the laws of physics 5. Model-based Optimization and Planning Leverage Prediction to Improve your Future 29
  • 30. Green IT and Storage, Energy and the Industry Storage is a notable contributor to Data Center energy consumption Data storage is projected to increase 6- fold between 2007 to 2011(1) “Building the Green Data Center” © 2008 SNIA All Rights Reserved Industry Concerns today Fear of „Green Washing‟ – lack of industry wide comparisons tools Inappropriate comparisons of technologies – Apples to Oranges New technologies being introduced – how will they effect energy usage? Benefit of product features vs. bigger picture of data management (1) IDC White Paper, “The Diverse and Exploding Digital Universe,” March 2008.
  • 31. Energy Cost of Data Storage 50,000 3,000 45,000 40,000 2,500 Capacity (PBs) 35,000 2,000 30,000 $M 25,000 1,500 20,000 15,000 1,000 10,000 500 5,000 0 0 99 00 01 02 03 04 05 06 07 08 09 10 11 19 20 20 20 20 20 20 20 20 20 20 20 20 Installed # of Petabytes (57% 2006-2011 CAGR) Cost to Power and Cool (19% 2006-2011 CAGR) IDC #212714, “The Real Costs to Power and Cool All the World's External Storage” – June 2008 Dave Reinsel Chart used by permission of IDC
  • 32. What Impacts Energy Consumption for Data Storage Storage capacity / usage efficiency increasing data  larger capacity  more disks redundant copies  magnify capacity needs variability in usage and utilization  inefficient allocation of space What is valuable data? What is the retention policy? Data transfer rate / access speed high I/O bandwidth  higher rotational speed; striping across many drives low access times  faster actuators; higher rotational speeds; caches How fast and immediate must data be available? (time-to-data) Data integrity 25% of “digital universe” is unique, but 75% are replicas / duplicates partly to ensure data integrity and survivability; partly wasteful Data availability / system reliability RAID uses extra drives, plus redundant power supplies, fans, controllers, How valuable is data? How likely are failures? How fast must data be available?
  • 33. Potential Paths to “Green” Storage Improve usage efficiency must be driven by De-duplication metrics / standards / guidelines Thin provisioning Minimize energy consumption Improved component designs – high-efficiency power supplies, advanced & flexible drives Variants of MAID – idle and spin-down New technologies Solid state storage Alternative + hybrid system designs (opportunity to rethink)
  • 34. Anatomy of a Storage System System design, complexity and Switches redundancy vary depending on applications & usage Apps Software Component designs, software features, and Appliances workload affect power consumption and efficiency Power Supplies Disk Arrays Fans Controllers PDUs Power Distribution Unit Hard drives UPSs Uninterruptible Power Supply
  • 35. Storage – Power Supply Efficiency 1 - Redundant power supplies are standard, except in the smallest systems Power Supplies (for servers)* Fans *presented by EPA at ENERGY STAR Computer Server Stakeholder Meetings; July 2008 Controllers 2 - Significant mechanical components, require Hard drives dual-output power supplies (12V, 5V) 3 - Power supplies often custom- designed for reliability
  • 36. Idle Power versus Active Power Idle Mode for a Storage Array storage system is protecting data, ready to process IOs background maintenance & optimization tasks on-going factors: time-to-data, overhead electronics, fan, maintenance systems are idle large fractions of the time Active Mode for a Storage Array storage system is carrying out IOs background tasks continue in parallel factors: workload (seq/random), response time, throughput evaluate a variety of workloads, plus sustained peak power
  • 37. HDD Capacity versus High Performance Capacity focused on GB/watt at rest 1 TB SATA: 15W 4 x 250 GB FC: 64W also tend to have better $/GB NOTE: power use is quadratic with respect to rotational speed Use the slowest drives that will fit your needs Performance focused on seek time 1 TB SATA: 12 – 15 ms 300 GB FC: 3 – 4 ms also designed for higher RAS * environments * RAS = Reliability, Availability, Security
  • 38. SSD vs HDD Power Value - Significant Power Savings 15k RPM Enterprise HDD SSD Idle Temp Load Temp 85°F 94°F SSDs reduce energy cost to 6.8W 0.5W operate and cool the data center 10.1W 0.9W Idle Power Load Power ~38% Less Heat, ~90% Less Power
  • 39. Storage Taxonomy for Energy Measurement Need a taxonomy (product classification) to enable fair comparisons among similar storage products e.g. for motor vehicles – motorcycles, cars, trucks Similar green metrics may apply to all product categories, but different values establish best-in-class Unique considerations apply to special categories e.g. amphibious cars, skid steer loaders, tanks Clear taxonomy will simplify comparisons and aid regulatory efforts
  • 40. SNIA Measurement Standard - Draft Storage taxonomy Measurement conditions Idle metric Active metric(s) Reporting results
  • 41. 1) Storage Taxonomy (1 of 2) Online Storage Near Online Storage Prime storage, able to serve random as well as Intended as second tier storage behind Online sequential workloads with minimal delay Storage. Able to service Random and Storage Taxonomy Summary Sequential workloads, but perhaps with noticeable delay in time to 1st data access. Maximum Capacity Guidance Note: Maximum Capacity Guidance reflects the maximum capacity a given offering can be purchased with and/or field upgraded to. It is intended to be used as a guideline as apposed to an absolute value. There will be case where a device may have greater or small capabilities, but otherwise is an appropriate match for a given classification due to Max Storage Devices Max Storage Devices other criteria, e.g.: redundancy capabilities Group 1) SoHo & Consumer Storage which is designed primarily for home (consumer) or home / small office usage. Up to 4 Devices –Often Direct Connected (USB, IP, etc) –No option for redundancy (will contain SPOFs) Group 2) Entry, DAS, or JBOD Storage which is dedicated to one or at most a very limited number of servers. Often will not include any More than 4 Devices Up to 4 Devices integrated controller, but rely on server host for that functionality. –Often Direct Connected (SATA, IP, etc.) –May optionally offer limited number of redundancy features Group 3) Entry / Midrange SAN or NAS connected storage which places a higher emphasis on value than scalability and More than 20 Devices More than 4 Devices performance. This is often referred to as „Entry Level‟ storage. –Network connected (IP, SAN, etc.) –Has options for redundancy features Group 4) Midrange / Enterprise SAN or NAS connected storage which delivers a balance of performance and features. Offers higher level More than 100 Devices More than 100 Devices of management as well as scalability and reliability capabilities. –Network connected (IP, SAN, etc.) –Has options for and often delivered with full redundancy (no SPOF) Group 5) Enterprise / Mainframe Storage which exhibits large scalability and extreme robustness associated with Mainframe deployments, though are not restricted to Mainframe only deployments. More than 1000 Devices –Mainframe connectivity with optional network connection (IP, SAN..) –Always delivered with full redundancy (no SPOF) –Often Capable of non-disruptive serviceability See: Green Storage Power Measurement Specification for complete details
  • 42. 1) Storage Taxonomy (Continued: 2 of 2) Removable Media Virtual Media Infrastructure Infrastructure Libraries Libraries Appliances Interconnect Archival storage used in a Storage which simulates Devices placed in the storage SAN Devices which enable a SAN or Storage Taxonomy Summary sequential access mode. A Typical example would be Tape removable Media Libraries. Will typically use non tape or network adding value through one or more dedicated Storage other Storage Network data switching or routing. based archival, both Stand Along based storage and as such are enhancements. Examples include: (Continued) and Robotically assisted libraries. able to respond to data SAN Virtualization, Compression, requests more quickly De-duplication, etc. Maximum Capacity Guidance Note: Maximum Capacity Guidance reflects the maximum capacity a given offering can be purchased with Max Storage Devices and/or field upgraded to. It is intended to be used as a guideline as apposed to an absolute value. There Max Tape Drives Max Port Count will be case where a device may have greater or small capabilities, but otherwise is an appropriate match Supported* for a given classification due to other criteria, e.g.: redundancy capabilities Group 1) SoHo & Consumer Note: * Infrastructure Appliances by Stand Alone Drive definition have no intrinsic storage, Storage which is designed primarily for home (consumer) or home / small other than what is used for local office usage. (No Robotics) processing and/or local Cashing of –Often Direct Connected (USB, IP, etc) data. –No option for redundancy (will contain SPOFs) Storage Devices Support in this case refers to the number of storage Group 2) Entry, DAS, or JBOD devices controllable down stream of the Appliance Storage which is dedicated to one or at most a very limited number of servers. Often will not include any integrated controller, but rely on server Up to 4 Drives Up to 32 host for that functionality. –Often Direct Connected (SATA, IP, etc.) –May optionally offer limited number of redundancy features Group 3) Entry / Midrange SAN or NAS connected storage which places a higher emphasis on value Support for up to 20 than scalability and performance. This is often referred to as „Entry Level‟ More than 4 Drives Up to 100 Devices Up to 128 Devices storage. –Network connected (IP, SAN, etc.) –Has options for redundancy features Group 4) Midrange / Enterprise SAN or NAS connected storage which delivers a balance of performance More than 100 Devices Support for more than 20 and features. Offers higher level of management as well as scalability and More than 24 Drives More than 128 Devices reliability capabilities. –Network connected (IP, SAN, etc.) –Has options for and often delivered with full redundancy (no SPOF) Group 5) Enterprise / Mainframe Storage which exhibits large scalability and extreme robustness associated with Mainframe deployments, though are not restricted to Mainframe only More than 100 Support for more than More than 11 Drives deployments. Devices 100 Devices –Mainframe connectivity with optional network connection (IP, SAN..) –Always delivered with full redundancy (no SPOF) –Often Capable of non-disruptive serviceability © SNIA 2009 See: Green Storage Power Measurement Specification for complete details
  • 43. Desired Storage Metric – “Productivity” Many possible definitions – must balance simplicity against applicability • “typical workload”, with levels • detailed performance benchmark – results/W Standard Performance Evaluation Corporation • “four corners”, maximum performance, maximum power Random, Sequential, • The Green Grid Productivity Proxy Proposals write read example – Proxy #4 – bits/kilowatt-hour Random, Sequential read write
  • 44. Complications Server power Storage power • Max power =/= Max performance SPECweb 2005 (banking) + storage • Significant whole-system considerations Single disk drive power profile “Storage Modeling for Power Estimation”, Miriam Allalouf , Yuriy Arbitman, Michael Factor, Ronen I. Kat, Kalman Meth, and Dalit Naor; IBM Haifa Research Labs; manuscript; March 2009 IBM Haifa Research Labs “The Next Frontier for Power/Performance Benchmarking: Energy Efficiency of Storage Subsystems” Klaus-Dieter Lange; SPEC Benchmark Workshop 2009; January 2009
  • 45. Need for Data Redundancy RAID 10 – protect against multiple disk failures DR Mirror – protect against whole-site disasters Backups – protect against failures and unintentional deletions/changes Compliance archive – protect against heavy fines Test/dev copies – protect live data from mutilation by unbaked code Overprovisioning – protect against volume out of space application crashes Snapshots – quicker and more efficient backups
  • 46. Result of Redundancy - Power consumption is roughly linear in the number of naïve (full) copies Test Test 10 TB Test Test Test Archive Archive ~10x + Backup Backup Backup Snapshots Snapshots Snapshots Snapshots 5 TB “Growth” “Growth” “Growth” “Growth” RAID10 RAID10 RAID10 RAID10 Data Data Data Data Snapshots Snapshots Snapshots Snapshots Snapshots “Growth” “Growth” “Growth” “Growth” “Growth” “Growth” RAID10 RAID10 RAID10 RAID10 RAID10 RAID10 RAID10 1 TB Data Data Data Data Data Data Data Data App RAID 10 Over- Snap- DR Disk Compliance Test/Dev Data Overhead provision shots Mirror Backup Archive copies
  • 47. Positive Effect of Green Storage Technologies - Green storage technologies use less raw Test capacity to store and use the same data set 10 TB Test Test - Power consumption falls accordingly Test Test Test Test Test Test Test Test Test Test Test Test Test Archive Test Test Test Test Test Test Test Backup Archive Snapshots Test Test 5 TB “Growth” Backup Archive Archive Archive RAID10 Snapshots Backup Backup Backup Archive “Growth” Backup Data Snapshots Snapshots Snapshots Snapshots RAID DP “Growth” “Growth” “Growth” “Growth” Data RAID DP RAID DP RAID DP RAID DP Snapshots Data Data Data Data “Growth” Snapshots “Growth” Snapshots Snapshots Snapshots Snapshots RAID10 1 TB RAIDDP “Growth” RAIDDP “Growth” RAIDDP “Growth” RAIDDP “Growth” RAIDDP Data Data Data Data Data Data RAID 5/6 Thin Multi- Virtual Dedupe Provisioning Use Clones & Backups Compression
  • 48. Green Storage Technologies Enabling technologies Storage virtualization Storage capacity planning Green software Compression Snapshots Virtual (writeable) clones Thin provisioning Non-mirrored RAID Deduplication and SIS Resizeable volumes
  • 49. Typical Savings Thin provisioning 40 - 60% Average 30% utilization  over 80% utilization RAID 6 35% For 14-disk RAID 6 set, compared to RAID 1/10 Deduplication 40 – 95%, depending on dataset and time interval ~ 40 – 50% average over time Resizeable volumes 20 – 50%
  • 50. Green Storage Technologies (cont.) Other storage technologies and power saving techniques Capacity vs. high performance drives ILM / HSM MAID SSDs Power supply and fan efficiencies Facilities-side technologies Hot aisle/cold aisle Water & natural cooling Flywheel UPSs
  • 51. Savings Matrix Savings can multiply in combinations with checkboxes C SS VC TP R DD RV Compression (C) Snapshots (SS) Virtual Clones (VC) Thin Provisioning (TP) RAID (R) Deduplication (DD) Resizeable Vols (RV)
  • 52. SNIA Green Efforts SNIA Green Storage Initiative (GSI) and SNIA Green Storage Technical Work Group (TWG) on-going efforts to develop data-driven green standards & metrics power measurements at multi-vendor “unplugged” fests alliances with other active green organizations (The Green Grid, 80PLUS/Climate Savers, DMTF, SPEC, SPC) collaboration with EPA on the ENERGY STAR for Storage program Whitepapers / workshops four tutorials at SNW; online tutorials available (www.snia.org/education/tutorials) white papers from GSI
  • 53. Cloud Computing and Storage
  • 54. IDC: Worldwide IT Cloud Services Spending*/** $5.5 billion Storage Storage 5% 13% Server 9% Business Server Business Applications 8% Applications 57% 52% App Dev & Deployment 11% App Dev & Deployment 9% Infrastructure Infrastructure Software Software 18% 18% 2008 2012 $16.2 billion $42.3 billion * by Product/Service Type, 2008 & 2012 ** Includes enterprise IT spending on Business Applications, Systems Infrastructure Software, Application Development & Deployment Software, Servers and Storage Source: IDC - IT Cloud Services Forecast - 2008, 2012: A Key Driver of New Growth
  • 55. Some basic cloud storage attributes Pay as you go Self service provisioning Scalable, Elastic Rich application interfaces No need for consumers to directly manage their own storage resource By offloading the Storage Management, data owners can focus more on the management of data requirements ...
  • 56. Cloud Computing Perceived Benefits and Demand Drivers Cloud computing‟s “nirvana-like” Which in turn puts pressure on promise drives higher service the enterprise data center to level expectations among deliver higher service quality (at business entities and individual lower cost) Business Entities users IT Users IT Providers Key Benefit: Key Benefit: Key Benefit: Innovation Quality of Experience Competitivenes Faster, easier innovation Speed of access Lower TCO New business models Ease of access (anywhere, Faster Time to Market New products and services anytime) Higher Cust Rentention Faster time to market Ease of use Service quality Lower IT cost Minimal software requirements Resource optimization Lower IT risk (brand on access device Resiliency protection) No long-term commitments Flexibility Improved IT user productivity Efficiency Improved Client Satisfaction “Green” Improved Disaster Recovery Enhanced chargeback
  • 57. What is Cloud Storage? Cloud Storage can be contrasted with SAN/NAS storage Both are “Storage Networking” Provisioning may be different (some interfaces do not require this) How you pay for it may be different One primary difference is that essential management tasks for storage resources are performed by the Cloud operator and not the storage user Public Storage Clouds Latency may be an issue for most enterprise applications Primarily aimed at web-facing applications that already serve data over the web Importance of SLA Management Private Storage Clouds Can be either web-facing or used for enterprise applications Can be operated by internal IT departments – driving costs down and achieving better utilizations Importance of SLA Management Hybrid use of public and private clouds (including existing data centers) This is not only about capacity provisioning Data Assurance, Security, Delivery, Migration… Leverage Virtualized and Self*/Automated Management Environments Also part of Virtual Data Centers
  • 58. Some Examples of Cloud Interfaces De facto and proprietary interfaces Amazon S3 (http://aws.amazon.com/s3) “As simple as possible, but no simpler” GoGrid (http://wiki.gogrid.com/wiki/index.php/Cloud_Storage) Some offer standard data path APIs, but allocation and provisioning are behind “storefronts” or proprietary APIs SAMBA, RSYNC, SCP – “standard” open source Microsoft Azure Interface De jure APIs WebDAV (http://www.ietf.org/rfc/rfc2518.txt) iSCSI (http://www.ietf.org/rfc/rfc3720.txt) NFS (http://www.ietf.org/rfc/rfc3530.txt) FTP (http://www.ietf.org/rfc/rfc959.txt) But very few of these interfaces support the use of metadata on individual data elements
  • 59. Cloud Storage: Use Cases and Requirements Store my file and give me back a URL (i.e. Amazon S3) Best Effort Quality of Service? Provision a filesystem and mount it (i.e. WebDAV) Quality of Service specification via provisioning interface Give me Filesystems/LUNs for my Cloud Computing NAS box in the cloud… Store my backup files until I need them back Maybe offer me a local cache as well Archive my files in the Cloud for Preservation/Compliance Maybe offer me eDiscovery services, “tape in the mail” retrieval Store all my files, allowing me to set the Data Requirements, let me cache and distribute geographically Policy driven Data Services based on Data System Metadata markings
  • 60. Types of APIs Besides the “Data Path” APIs (previous slide), there are other interfaces that Cloud Storage may require E.g. Storage Provisioning For certain types of data storage interfaces (block, file) from the cloud you will need to provision/allocate storage before you can use it This provisioning can be done via a UI or an API Existing standards can be leveraged (e.g. SNIA SMI-S) E.g. Storage Metering Since the cloud storage paradigm is “pay as you go”, you need to know what your bill will be at the end of the billing cycle What operations affect my bill? UI typical, but an API standard would enable interoperability and better automation Telecom Industry Practice – every transaction has a “Call Detail Record” that is aggregated for billing
  • 61. Some Example Data Storage Interfaces Block Interfaces SCSI, ATA, IDE Local File Interfaces POSIX, NTFS Network File Interfaces NFS, CIFS, SMB2, Appletalk, Novell, AFS Object Based OSD, XAM Database JDBC, ODBC Not all of these make sense for the Cloud
  • 62. Cloud API to the Resource Domain Model Cloud interfaces with all 3 domains (Information, Data, Storage) Integration of services with different type of Clouds (Compute, Applications...) Federation of Clouds Cloud Exchange, Cloudbursting… Data Movement Migration, Delivery, Regulations
  • 63. XAM API: an example Data Storage Interface XAM is the first interface to standardize XAM User metadata is un- system metadata for retention of data interpretable by the system, but stored with the other data and is XAM implements the basic capability available for use in queries to Read and Write Data (through Xstreams) Given this we can see that XAM is XAM has the ability to locate any a data storage interface that is XSet with a query or by supplying used by both Storage and Data the XUID Services (functions) XAM allows Metadata to be added to the data and keeps both in an XSet object XAM uses and produces system metadata for each XSet For example Access and Commit times (Storage System Metadata) But it also uniquely specifies Data System Metadata for Retention Data Services
  • 64. Standards for Cloud Storage Service access interfaces Storage service interfaces Cloud Service User Service Management Virtual Image Management Provisioning QOS SOA Application Performance management Middleware Chargeback accounting Data protection Storage Security Virtualized Infrastructure Server / Storage / Network Compute Storage infrastructure management interfaces (SMIS)
  • 65. SNIA Cloud Technical Work Group www.snia.org/cloud Engaging the industry http://groups.google.com/group/snia-cloud Alliances Education & Whitepapers Use Cases & Taxonomy Interface Specification And coming soon to Brazil! Cloud Storage Brasil http://groups.google.com/group/snia-cloud-br?hl=pt-br
  • 66. Thank You PRESENTATION TITLE GOES HERE Muito Obrigado! www.snia.org www.snia.com.br

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