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Invicta accelerated storage clle-2014

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  • How do we know? We know that decision making within businesses is moving from strategic, where there was plenty of time to analyze and really think about everything to tactical. The tactical is emerging because information is flowing very, very quickly from a variety of sources, and so businesses are tempting to move more than automated style, if you will, of information acknowledgement and faster decision making. Businesses have a need to respond in real time. Data Analysis enables companies to focus less on what happened (its automated) and more on why it happened. Drill down in the OLAP process… Modeling and Predicting Leverage information for predictive purposesUtilize advanced data mining and predictive power of algorithmsReal-Time, Operational Analytics Information must be extremely up to date Query Response times must be measured in seconds to accommodate real-time, operational decision makingAutomated Decision GovernanceMore and More decisions become executed with event driven triggers to initiate fully automated decision processes based on real-time data
  • We talk a lot about things such as big data, but in fact, we have more data that's coming much faster and it's much more complex. The complex comes from the variety of sources that come together. That could be data from Facebook or Instagram coupled with information systems, may be coupled with some data that may actually be coming out of the supply chain itself. All of this has to be aggregated and pulled together much faster than it has been.This is what everyone likes to talk about. – (Big Data) Big Data is comprised of continuously collecting data from many sources but needs to be acted on quickly. Analytical overload causes mission critical problems.“Big data is part of an iterative life cycle that should be part of an over-arching enterprise information strategy” – Pete Tseronis, Department of Energy “There’s a risk of getting lost in a sea of detail or spending too much time thinking about volume, and not enough time thinking about actual utility or value” – Mike Olson, Cloudera“At the heart of big data: “Funding and prioritizing the need to solve a real mission-critical problem.” – Van Ristau, DTL Solutions
  • So Reducing time-to-value, Understanding what needs to be done is becoming a competitive table stake for many, many companies. They have more data, they need to manipulate it faster and they need to do this to get to a better decision much faster than they ever had before. Carpe Diem – seize the moment. In reality, you need to seize the moment so whenever possible you can make close to the moment decisions. Decisions may not always be made by humans. Transactions are happening, fast intelligent analysis needs to occur so you can make best-in-class business decisions. The world is bigger than data. Need the ability to have more data, act on that data faster, then that is what leads you to faster decisions. Take BIG DATA –transform it to FAST DATA to get QUICKER DECISIONS and INSIGHTS!BIG DATA CIRCLE: (Data coming from many different places)More data leads to more decisions…becoming more informedMore data from more sources is a robust collection of individual data points. (Richer knowledge set) CollectFAST DATA CIRCLE:Acting on data quickly leads to faster decisions…getting an understanding of your information fasterTransact, Process and Analyze your individual data points to compile business intelligence ActRich Information (outcome of faster decisions)Faster decisions create a competitive advantage…leading to an outcome, a decisions Make decisions that turn opportunities into sustained competitive advantageDecide
  • We built UCS to:reduce time to provision, cabling, management costs, power and cooling costs and offer world-record application performance benchmarks. Customers still told us they were struggling to apply flash/solid-state technology in the environmentUSC is about the platform, and making computing simpler to manage. Infrastructure extracted, assets added faster, UCS manager allows you to organize and reorganize quicker.It still doesn’t solve customers end to end problem. Which is where customers left their data. Need to get it quickly. It needs to be consistent. HDD cant do it, causing a gapIn the 52 weeks in a year, how many do you want to spend configuring the infrastructure. Answer=0. Every week you spend is a week you cant do business at the rate you want to. UCS Invicta plugs in the missing piece to achieve this.Here’s how:Data Acceleration Layer for ApplicationsAddress scale of new data setsAddress new velocity requirementsManage as a SystemSimplify infrastructure to drive TCOMulti-Workload / Multi-Tenant Flexibility
  • Reduction in Provisioning TimesReduction of CablingReduction of Ongoing Management CostsReduction of Power and Cooling CostsWorld-Record Application Performance Benchmarks
  • The UCS Founding principles are strikingly similar to the UCS Invicta Series Solid State Systems principles.The addition of flash memory to UCS’s already serves and network/storage access create UCS. The combined solution benefits are the integration of performance characteristics much closer to the computing domain. (this changes milliseconds changes into microseconds) (Give an example of what this can translate to in business terms) Reduction of either a batch cycle for many hours that runs in minutes, or a long running report that now runs immediately. (Giving them the opportunity to do something they couldn’t do before) ie – TTX story – 48 hour process that now runs in 3 years.
  • Reduction in Provisioning TimesReduction of CablingReduction of Ongoing Management CostsReduction of Power and Cooling CostsWorld-Record Application Performance Benchmarks
  • Reduction in Provisioning TimesReduction of CablingReduction of Ongoing Management CostsReduction of Power and Cooling CostsWorld-Record Application Performance Benchmarks
  • Title changed
  • Assumed IOPS for 15K HDD= 200 IOPsAssumed IOPs for 7.2K HDD= 70 IOPsAssumed IOPs for unmanaged Flash Drives = 35,000 IOPs
  • Symmetrical I/O occurs because we take random data and we actually make it sequential. By making it sequential, you make it highly efficient for flash. A few things occur. We can write to the media very, very quickly while protecting it. We also reduce what's referred to as wear on the media because we're not really allowing the flash itself to be involved and determining how that information will be written. We handle all of that.Many of our competitors don't directly manage the flash. They allow the actual local flash media to make all kinds of decisions about how the data should be applied, which is not very efficient at all.
  • We solve three basic problems, and we address three basic objectives. The first is workload acceleration. Applications come on to our platforms, and they go faster. More data can be consumed, and it can be done very, very quickly (low latency). Next, we can eliminate redundant data. By eliminating redundant data, we can actually store more information on our technology making our platforms very efficient. Then finally for the datacenter, energy consumption and floor space, are both reduced, meaning we consume less of it, and management overhead is reduced because there is just less to manage with our technology it’s much simpler use.
  • Our portfolio consists primarily of two products. The t appliance has very high performance, has very good bandwidth, and it has very good storage characteristics. That foundation appliance can be deployed as parte of a UCS Invicta Scalable System, allowing the customer to scale on both performance and capacity axes. It's scalability, modularity, the ability to accelerate an application, the ability to optimize the data, also to handle multiple workloads at the same time, which can all be done without sacrificing performance.
  • Need to update throughput numbers in all casesWhat are our two sets of building blocks, if you will? If you have the ACCELA appliance, you’re really looking at the ability to either accelerate workloads or reduce the amount of data that were actually stored. It's a unique offer. We're really the only vendor in the marketplace today that allows our customer to choose the kind of appliance they would like to use. Those same building blocks, enfold into our UCS Solid State Systemstechnology, become what we refer to as silicon nodes. The customer can choose one or the other, or they can actually combine them under the same architecture. Of course what you can see here are the performance and capacity characteristics.
  • Performance numbers are based upon 4K block over FC connection. A Note from Engineering: Benchmarking a data reduction node will never by 100% accurate. Performance will vary based on use case. Engineering advice is to use DR performance numbers as a guideline. Engineering has indicated as we continue to test various models we will share key findings. ---What are our two sets of building blocks, if you will? If you have the appliance, you’re really looking at the ability to either accelerate workloads or reduce the amount of data that were actually stored. It's a unique offer. We're really the only vendor in the marketplace today that allows our customer to choose the kind of appliance they would like to use. Those same building blocks, enfold into our UCS Solid State Systems technology, become what we refer to as silicon nodes. The customer can choose one or the other, or they can actually combine them under the same architecture. Of course what you can see here are the performance and capacity characteristics.
  • When we combine it all together, what we adopt with here is a Scale Up/Scale Out architecture. You can start as little as pair of routers and a pair of nodes. If you decide that you need to drive more throughputs to the nodes, you can add more routers. If you decide that you would like to add more performance and capacity, you can add more nodes. Every time you add a node, you're adding CPU, memory, operating system, independently managed function, and most importantly, flash management. This is what sets us apart from many of our competitors.
  • Need to update throughput numbers in all casesWhat are our two sets of building blocks, if you will? If you have the ACCELA appliance, you’re really looking at the ability to either accelerate workloads or reduce the amount of data that were actually stored. It's a unique offer. We're really the only vendor in the marketplace today that allows our customer to choose the kind of appliance they would like to use. Those same building blocks, enfold into our UCS Solid State Systemstechnology, become what we refer to as silicon nodes. The customer can choose one or the other, or they can actually combine them under the same architecture. Of course what you can see here are the performance and capacity characteristics.
  • The INFINITY architecture, when broken out, looks like this. We have a switched fabric located at the top, and acceleration or data reduction nodes at the bottom. You can scale multiple routers and multiple nodes in a variety of combinations.
  • In terms of laying out how our products sort of stack in terms of features, what you'll notice is that most of them have the same features, except when we get into UCS Solid State Systems and Scale Up/Out architecture, you'll notice that there are a couple of additions. We can provide things such as mirroring. When you move over to INFINITY, we actually have this switch fabric, and that's what allows you to scale to the increased number of routers and an even greater number of nodes. This is what allows an organization to really bring all of their applications, if you will, into our platform regardless of what needs data reduction or IO and throughput.
  • Pooled ResourceDeterministic performanceOperational Efficiency
  • Let's talk a little bit about the workloads themselves. We know that the world wants to go faster, that that's what we need workloads to do. We know now we have a technology, but what can we bring to bear? Any of these types of workloads, plus many, many others, will benefit from acceleration and/or optimization. Virtual desktops will certainly benefit from both. Batch processing and database will traditionally benefit from higher acceleration, analytics intelligence as well. In something such as Cisco UCS Invicta, all of these applications can run together.Lower Power Consumption Lower Cooling costs.Consume Less floor spaceConsolidate DatabasesLower Licensing, Support & Maintenance Apps DatabasesVirtual DesktopsEmailWorkloads Batch ProcessingQueriesData LoadsOLTPSimplicity: Install it and use itDoesn’t require performance tuningPlug and Growadd additional resources as you need them
  • Unified Computing with integrated data acceleration layer: No other infrastructure vendor has this capability todayCompetitors are still trapped with siloed architectures of compute, networking, storage and now flash/solid-state

Transcript

  • 1. Local Edition Invicta Accelerated Storage Richard Nitz Consulting Systems Engineer
  • 2. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Agenda • Introduction • Business Drivers • It’s Time to Rethink • Flash Changes Everything • Invicta OS • Cisco UCS Invicta Series Overview • UCS Next Gen • Managed by UCS Director • Conclusion 2
  • 3. Local Edition Business Drivers
  • 4. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition 2000 2005 2010 2015+ D a y s H o u r s M i n u t e s S e c o n d s Response Time Decision Making Data Analysis Modeling and Predicting Real-Time, Operational Analytics Automated Decision Governance Real-TimeStrategic
  • 5. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition
  • 6. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition
  • 7. Local Edition It’s Time to Rethink
  • 8. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UCS Founding Principles Servers UCSNetwork/ Storage Access Application Centric Operational Simplicity Platform for IT Innovation
  • 9. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Sources: Cisco UCS Changing the Economics of the Datacenter, Customer Case Studies, Cisco UCS Performance Benchmarks Superior Economics and Operations Superior Performance 82 World-Record Application Performance Benchmarks 54% Reduction of Power and Cooling Costs 61% Reduction of Ongoing Management Costs 77% Reduction of Cabling 84% Reduction in Provisioning Times
  • 10. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UCS Founding Principles Servers ? UCSNetwork/ Storage Access Application Centricity Operational Simplicity Platform for IT Innovation
  • 11. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UCS Founding Principles Servers Flash Memory UCSNetwork/ Storage Access NEXT GEN UNIFIED COMPUTING Integrates Solid-State Memory Systems into UCS Fabric UCS Invicta Series Solid-State Systems Application Centricity Operational Simplicity Platform for IT Innovation Address new data velocity and scale requirements Integrate application acceleration into the computing domain
  • 12. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Sources: TechValidate – validated customer research Superior Economics 44% Reduced spend on energy (power & cooling) 46% Eliminated business application process delays 44% Reduced hard costs due to a smaller datacenter footprint 42% Reduced hard costs due to less management time
  • 13. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Sources: TechValidate – validated customer research Superior Application Performance 77% Improved scalability, reli ability & performance 76% Analytic & intelligence, batc h processing or database loads transacted faster 59% Experience faster transactions 58% Align high performance application workload data size with exact capacity requirements ✓
  • 14. Local Edition Flash Changes Everything
  • 15. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition 1E-09 0.000001 0.001 1 Second Millisecond Microsecond Nanosecond Response Time Slow Zone Fast Zone HDD Flash CPU
  • 16. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition PCIe • EMC • Fusion-IO • HDS/ Virident • LSI • Violin Appliance • Cisco • Astute • Nimbus • IBM • NetApp • Skyera • Violin Scale Up • Dell • HDS • HP • Nimbus • Pure Storage • EMC VNX-F Scale Out • Kaminario • SolidFire • EMC XtremIO Scale Up/Out • Cisco
  • 17. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Reference Architecture for 1,000 Desktops 1,000 Persistent Desktops will require: < 10TB of capacity ~80K backend IOPS 41 15K HDDs 25 7.2K HDDs 3 Flash Drives TOTAL IOPS: 114,950 TOTAL CAPACITY: 63.2 TB 3 types of drives, 3 types of RAID
  • 18. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Example UCS Invicta LUN Diagram Home Directory, Xen App & User Profiles SnapshotsInfrastructure Virtual Desktops Data Reduction Appliance for 1,000 Desktops 12 TB Raw TOTAL IOPS: 180,000 *50/50 read/write mix, refer to earlier slide “A Note on Numbers”
  • 19. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition 41 15K HDDs 25 7.2K HDDs 3 Flash Drives . 114,950 IOPS 63.2 TB 180,000 IOPS 64 TB** *refer to earlier slide “A Note on Numbers” **Effective Capacity
  • 20. Local Edition Invicta OS
  • 21. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Receive •Data blocks of Various size arrive from Hosts from network interconnects Protect •Data Blocks are stored in the power loss buffer and passed onto the Block Translation Layer (BTL) Optimize •The Block Translation Layer Aggregates and sizes Data Blocks for the RAID Layer and Flash Media •Hash tags are created for each block Commit •BTL Optimized Data is flushed across the RAID stripe and Flash erase blocks concurrently
  • 22. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Write the RAID stripe and the 2MB Erase Blocks concurrently
  • 23. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Inbound data blocks Cache Block Translation Layer Optimize Inbound data blocks Write Write Fill
  • 24. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition • Enabled for the entire UCS Invicta Appliance or individual UCS Invicta Scaling Array Nodes during the initial system build • In-line deduplication: Active 100% of the time, for every incoming write • Cannot be changed / turned off • Fixed block algorithm (4k size) • Overcommit ratio assumed for each system (limited by array/node memory) • Overcommit = amount of data logically mapped vs. physically stored
  • 25. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition
  • 26. Local Edition The Cisco UCS Invicta Series Product Overview
  • 27. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Workload Acceleration Fast I/O High Bandwidth Low Latency Data Reduction Eliminate Redundant Data Efficient Storage Utilization Data Center Efficiency Reduce Energy Consumption Reduce Floor Space Consumption Reduce Management Overhead
  • 28. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Up to 1.2 Million IOPS* Up to 12 GBps* Bandwidth Up to 240 TB Raw UCS Invicta Appliance UCS Invicta Scaling System Scalability Modularity Application Acceleration Data Optimization Multiple Workloads Tuning-Free Performance Shipping NOW 250,000 IOPS 1.2 GBps Bandwidth Up to 24 TB Raw *Read IOPS, refer to earlier slide “A Note on Numbers” *refer to earlier slide “A Note on Numbers”
  • 29. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Based upon UCS C Series Platform Invicta OS Version 5.0.0 1. Host Connectivity 2. Power-Fail Data Protection 3. Flash Media 1. Flash Management 2. Volume Management 3. RAID 4. Replication 5. Snapshots 6. Reporting 7. Data Reduction Mode 1. De-Duplication 2. Thin Provisioning 8. UCS Director Support 9. iSCSI & Fibre Channel
  • 30. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UCS Invicta Appliance Mode Application Acceleration Data Reduction Throughput (GBps) 1.2 1.2 100% Read IOPS 220,000 250,000 Blended IOPS (50%read/50%write) 205,000 180,000 100% Write IOPS 200,000 120,000 Latency (Microseconds) <100 <100 Size 2 RU 2 RU Max Capacity (TB) 24 TB Raw 64 TB** **Effective Capacity
  • 31. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Scaling System Routers Manages connectivity between Hosts and Scaling System Nodes Supports combination of Acceleration and Data Reduction Nodes Max System Capacity with 10 Scaling Nodes • 240 TB RAW • 640 TB Effective Scaling System Nodes Application Acceleration Mode or Data Reduction Mode Application Acceleration Mode • 3TB-24TB RAW Capacity per node Data Reduction Mode • 25TB-64TB Effective Capacity per node SSN SSN SSR SSN SSR SSN SSN SSN
  • 32. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Scaling System Node Mode Application Acceleration Data Reduction Throughput (GBps)* 1.2 1.2 Blended IOPS* (50%read/50%write) 200,000 150,000 Latency (Microseconds) <200 <200 Size 2 RU 2 RU Max Capacity (TB) 24 TB Raw 64 TB** **Effective Capacity *Estimate SSN
  • 33. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UP TO 10 UP TO 6 Point to Point Switched Scaling System Routers Scaling System Fabric Scaling System Nodes SSRSSR SSRSSR SSNSSN SSNSSN SSNSSN SSNSSN SSNSSN SSN SSN SSN SSN SSN SSN
  • 34. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Appliances to Scaling Array Scaling Array to Scaling Array Appliance to Appliance Appliance to Open Target Linux/Windows
  • 35. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Appliance Scaling System Architecture INVICTA OS 5.0.0 5.0.1 Symmetric Read/Writes ✔ ✔ RAID Protection ✔ ✔ Asynchronous Replication ✔ ✔ Snapshots – Copy on Write ✔ ✔ Mirroring ✔ Web Based UI/API ✔ ✔ Role Based Access Controls ✔ ✔ Data Reduction Option ✔ ✔ Thin Provisioning ✔ ✔ iSCSI, Fibre Channel ✔ ✔ Enhanced Data Protection ✔ ✔ VAAI Support * ✔ ✔ Ethernet, Fibre Channel ✔ ✔ Increase Performance and Capacity ✔ Switched Fabric 7 Nodes & Up Max. Routers 2 Max Nodes 10 * VAAI is only supported in performance nodes
  • 36. Local Edition UCS Next Gen
  • 37. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Mixed Density Racks/Blades UCS Central - UCS Manager - UCS Director Fabric Interconnects Batch, Data Loads Performance Nodes Routers Stateless UCS Servers with Virtualized Adapters Workload Acceleration and Data Reduction Nodes Virtual Desktops Email Data Reduction Nodes OLTP Analytics • High I/O Performance with Flash • Multi-tenant / Multi-Workload: big data and relational DB on one platform • (Re)Allocate pooled resources rapidly for real-time decision making
  • 38. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Analytics & Intelligence Batch Processing Database Loads Email OLTP Transcoding Virtual Desktops
  • 39. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Advertising Aerospace Automotive Construction Education Finance Government Services Retail Real Estate Manufacturing Insurance Hospitality Healthcare
  • 40. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Management Time Weeks Days Hours Minutes Processing Time Days Hours Minutes Seconds Current State Desired State Energy/Floor Space Consumption Highest Lowest
  • 41. Local Edition Managed by UCS Director
  • 42. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UCS Director Manual processes require days or weeks to deploy resources; increases operating costs Data center teams stay in rigid silos, with complex hand-offs between domains and teams Static resource allocation results in under-utilization of resources and inefficiency Reduces resource deployment from weeks to minutes Allows IT teams to work together; encourages teamwork resulting in competitive innovation Delivers unified automation and management for optimum utilization and efficiencies
  • 43. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition Compute • UCS B and C Series CIM-C for XML interface • HP, Dell server support Network • Cisco network support • Brocade Support • F5 Support Storage • UCS Invicta Series • NetApp FlexPod Data Center, FlexPod Express • 7-mode and cluster mode • EMC VMAX, VNX and VNX2 • VCE vBlock Hypervisor • VMware • Hyper-V • Scales to large data centers (50,000 virtual machines) • Software Developer Kit for partners and vendors • Heterogeneous support of infrastructure devices • Single pane of glass to manage physical, virtual and bare metal
  • 44. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition End-to-End IT Acceleration Assign Resources UCS Director Application Performance Provision Infrastructure Install UCS and UCS Invicta REPEATAS NEEDED
  • 45. © 2014 Cisco and/or its affiliates. All rights reserved.Presentation_ID Cisco Public Local Edition UCS Invicta Series Appliance Scaling System Storage Blade Invicta OS V5.x • De-Duplication • UCS Director Support • FCoE Hardware C Series • Flash Memory B Series • Flash Memory Management UCS Director UCS Manager Network VIC Fabric Interconnect UCS B/C UCS Invicta SAN/LAN2014-2015 Major Integration Steps Items in green are completed or in progress
  • 46. Local Edition