DriveScale End User Sales Presentation

DriveScale End User Sales Presentation
Software Defined Infrastructure for Hadoop and Big Data DriveScale End User Sales Presentation April 24, 2017

Presentation Overview
Introduction to DriveScale / Corporate Facts The Market Problem How DriveScale Solves the Problem SDI = Software Defined Infrastructure for Big Data Benefits of Software Defined Infrastructure Solution Overview & Architecture Reference Accounts Partners Competitors Summary FAQ ©2017 DriveScale Inc. All Rights Reserved.

Example Meeting Agenda
Presenter Role Topic Time John Doe Jane X Customer - VP Infrastructure VAR – Account Manager Meeting Kickoff 10 Ryan Shorter Jeff Chesson Howard Doherty DriveScale – Director of Sales Eastern US DriveScale – Director of Sales Central US DriveScale – VP Sales DriveScale Exec Summary and Key Benefits 20 Chris Munford Salah Chaou DriveScale - VP Field Operations DriveScale – Principal Solution Architecut DriveScale Solution Overview All Q&A ©2017 DriveScale Inc. All Rights Reserved.

Executive Summary: DriveScale
DriveScale is Software Defined Infrastructure for Hadoop and Big Data E.g. Spark, Cassandra, NO SQL, and other webscale apps enables the most efficient & agile infrastructure for Private & Hybrid Clouds Economical– Save up to 60% vs DAS, Centralized NAS/SAN, or Public Cloud. DriveScale brings HyperScale Hadoop Architecture to Enterprise Efficient – Get more compute and storage from your HW investment. Up to 3x improvement by pooling resources across clusters Easy – Uses same servers & drives; No changes to SW stack; Managed by Cloudera Director and HWX Cloudbreak; Improved performance vs alternatives – maintains data locality Ryan to mention that our founders invented Cisco UCS when talking about this slide.

About DriveScale Over the last 15 years, a new computing approach has emerged inside Internet scale companies such as Google and Facebook to handle more information and data than ever before. DriveScale is in the business of productizing this modern computing architecture, called Software Defined Infrastructure (SDI), and delivering to ALL enterprises. DriveScale SDI is disrupter to traditional ways of deploying compute and storage infrastructure by disaggregating and composing compute and storage through software, enabling a modern architecture that breaks through workload limitations without compromise. Our unique SDI solution is complementary to your current datacenter infrastructure and delivers efficiency and an easy experience in a cost effective manner. You no longer need to move to a costly public Cloud environment to enjoy efficiency and scale. We build on your IT investments to deliver lower costs, and higher efficiency and flexibility without compromising security, performance or reliability in a private cloud environment. Our solution is enterprise-ready today, and was built by renowned enterprise technologists from Sun, Cisco and HPE.

DriveScale Corporate Facts
Software Defined Infrastructure for Hadoop and Big Data Founded 2013, Sunnyvale, CA Founders: Satya Nishtala and Tom Lyon, Architects of Sun Workgroup Servers and Desktop, Cisco UCS and inventor of IP Switching Pelion Venture Partners, Foxconn, Nautilus Venture Partners Product Launched on May 19, 2016

Tom Lyon, Chief Scientist
Executive Team Gene Banman, CEO CEO: ClearPower, Zero Motorcycles, NetContinuum VP/GM Desktops, Sun Micro President of Sun Japan Satya Nishtala, CTO Fellow, System Architect: Nuova (Cisco UCS) DE, Lead architect: Sun - UltraSPARC workstations and work-group servers, Storage Tom Lyon, Chief Scientist Founder: Nuova (Cisco UCS), Founder: Ipsilon (Nokia) Emp #8: Sun Micro – SunOS, SPARC, SunScreen Duane Northcutt, VP Eng. CTO: Technicolor, Trident, Silicon Image VP Tech Kealia; DE and inventor of SunRay, Sun Micro PhD CS, CMU F O U N D E R S Howard Doherty, VP Sales VP Sales: Arkeia Software (WD), Symphoniq, NetContinuum Sales Mgr: Netscape, Cisco S.K. Vinod, VP Product Management Co-founder: Xsigo Systems Sun Micro, Product Management Xerox PARC, imaging system startup venture This is a snapshot of the executive team. Gene Banman is our CEO and knows the founders from his time as VP/GM of Desktops at Sun Microsystems. Satya Nishtala is a founder, also once from Sun, and more recently a Nuova Systems and Cisco UCS fellow. Tom Lyon is another founder, and a very early employee at Sun. He was also a cofounder of Nuova Systems and inventor of IP switching when at Ipsilon. Duane Northcutt is our third founder, and.. You guessed it! From Sun. He has also served as CTO at Technicolor and Silicon Image, as well as VP of technology at Kealia. Gene Slide

Co Founder and Former EVP
Advisors Amr Awadallah Founder/CTO Cloudera Scott McNealy Founder – CEO Sun Microsystems James Gosling Creator of Java Ameet Patel CTO JP Morgan, Morgan Labs Phil Roussey Co Founder and Former EVP Bell Micro We have several investor advisors that we also wanted to call out. In particular, Amr Awadallah is CTO of Cloudera and his interest in us is hugely validating. Scott McNealy was the CEO of Sun Microsystems and James Gosling was the creator of Java. Interesting personalities all. Gene Slide

The Big Data Market Problem
©2017 DriveScale Inc. All Rights Reserved.

The Big Data Market Problem:
How to deploy in a private or hybrid cloud efficiently? So, in order to alleviate these issues, you decide to move it all in-house and build your own Big Data infrastructure.

How to right-size your Big Data Infrastructure?
How Many Clusters? How Many Server Nodes in each Cluster? For each Server Node: How much CPU/cores? Memory? Storage/Drives? How can you change the cluster balance as workloads change?

The Problem: You must choose infrastructure FIRST
… before your applications are written (or deployed or scaled or evolved) Infrastructure Application These decisions will determine the success (and cost) of your Big Data plans

= = $20,000 The default approach…
Choose a “one size fits all” Server Node and buy many of them => = = $20,000 2U servers: The minivans of computing – versatile, but not the best at anything

But, One Size Does _not_ Fit All…
Each type of cluster “wants” a different amount of disk per server Hadoop Data Lake Dev/Test Hbase Kafka Cassandra … Fixed silos per cluster type lead to madness No resource sharing No elasticity Too many server types / SKUs

You want Choice AND Adaptability
4/256GB sportscar cargo plane CPU/RAM per rack unit minivan 1/8GB moped cargo truck If you are going to buy a lot of infrastructure, it would be nice to have a mix of choices rather than just minivans. Sometimes you want all performance and no storage, like a motorcycle Sometimes you want a lot of performance and just a little storage, like a sportscar Sometimes you want a balance of performance and storage, like a minvan. 500GB STORAGE 8 PB 15

The RIGHT Solution is Software Defined Infrastructure
Dynamically change your infrastructure to match your application workflow needs # of clusters # of server nodes per cluster # CPUs per server amount of RAM per server # disks per server DriveScale SDI allows you to Right-Size your Big Data Infrastructure “You can be wrong and still be right”

DriveScale Software Defined Infrastructure Solution Overview

Software Defined Infrastructure for Big Data
Requires 2 actions: 1) Server Disaggregation, separate servers into “compute” servers = diskless compute servers JBODs (just a bunch of drives) = ‘compute-less’ storage servers 2) Logical Server Composition Join “Compute” with “Storage” to create “logical” servers & clusters over Ethernet/IP top of rack switch dynamically change as needs change

Step #1: Disaggregate the Servers
Stop buying 2RU Servers with storage (NOTE: you can still use your existing servers) 2 RU 2 RU 2) Instead, start to buy (a) high density commodity compute servers and (b) JBODs for drive storage 2 RU 1 RU 12 RU 1 RU 2 RU 8 RU 1 RU 2 RU 5 RU JBOD (Just A Bunch of Drives) 2 RU The magic of DriveScale happens in two steps. The first part involves the separation of compute from storage wherein, you can purchase diskless or disk lite servers and simple JBOD’s. You can now buy compute when you need compute, and storage when you need storage.

Step #2: Compose Logical Server Nodes and Clusters
Cluster 1 - Performance - Spark Node 1 Node 2 Node 3 Node 4 Node 5 Cluster 2 – Storage – Data Lake Node 1 Node 2 Node 3 Physical Compute Servers Physical JBODs (Just a Bunch of Disks) Then, using DriveScale’s system, you put the lady back together by re-composing the compute with drives in any measures of each as you need for your particular workloads and clusters “Logical” Server Nodes and Clusters Right-sized for Big Data / Hadoop

Adapt over time as workloads change
Cluster 1 - Performance - Spark --- Add or Remove Nodes --- Node 1 Add Storage to nodes Node 2 Node 3 Node 4 Node 5 Cluster 2 – Storage – Data Lake --- Return Storage to pool --- Node 1 Node 2 Node 3 Physical Compute Servers Physical JBODs (Just a Bunch of Disks) Then, using DriveScale’s system, you put the lady back together by re-composing the compute with drives in any measures of each as you need for your particular workloads and clusters “Logical” Server Nodes and Clusters Right-sized for Big Data / Hadoop

Shown another way… Create a third Cluster Z Expand Cluster X
Create a Cluster X Create another Cluster Y Shown another way… So you can create a cluster for one workload… Then create another for a different workload… Then expand the first cluster with additional compute and storage resources to accommodate changing workloads.. And Create a third cluster…..and so on.

DriveScale scales to 1000s of nodes+ … keep JBOD storage local in each rack
DriveScale Adapter DriveScale Adapter DriveScale Adapter DriveScale Adapter DriveScale Adapter Cluster 1 “Balanced” compute DriveScale Adapter DriveScale Adapter Cluster 2 Storage focus JBOD storage Cluster 3 Compute focus For small deployments, a cluster can remain in one rack. For large deployments, you can stripe clusters horizontally across racks, and grow a cluster to thousands of nodes. In either case, each node has its disk in the same rack and uses untapped top-of-rack switching resources.

You just built a private cloud
Now you have a Private Cloud All hardware resources are shared DriveScale makes your private data center operate like a public cloud for Hadoop When you achieve this separation and re-composition capability, nothing in this infrastructure is fixed any more. Every component is part of a truly elastic infrastructure, building blocks waiting for you to put together in any configuration of your choosing. This means that you now have a true ‘Private Cloud’ where all the hardware resources are shared and can be deployed at will. Your Infrastructure is Highly Elastic

SDI Revolutionized Hadoop Storage
3 Good 2 Fair 1 Poor Storage Type DAS (Direct Attached Storage) DriveScale SW Defined Infrastructure Centralized storage NAS or SAN Comments Cost 5-10x Buy disks, instead of proprietary ‘appliances’, which are 5x to 10x the cost. Don’t waste money on storage features Hadoop doesn’t need (dedup, RAID, erasure encoding, etc.) Performance 1/2 - 1/4 Give Hadoop nodes direct access to rack local disks, not shared or centralized file systems with limited IO Bandwidth Utilization 30-50% Buy only the disks you need, pooled local to the rack. This allows better storage utilization and re-balancing disks across nodes in a cluster gives better CPU utilization, putting more servers to work. Adaptability (ability to change node storage) none Re-define your server and storage infrastructure as application needs change. Scalability anti-hadoop Give nodes direct access to their own disks. Don’t share file systems (“nothing shared”). 2 3 1 3 3 1 1 3 3 1 3 2 3 3 1

Benefits of Software Defined Infrastructure

DriveScale Target Customers
Big Data Applications – Hadoop, Spark, Cassandra, NoSQL, etc On-Premise Applications – Private and Hybrid Public Cloud Concerned about infrastructure costs & wasted spend Application profiles are dynamic – where infrastructure requirements are hard to predict and always changing Approaching Power / Rack Space Limits Want to share infrastructure across multiple applications Want to buy storage and compute independently (storage or compute bound)

DriveScale Target Customers – Common questions/statements
How can I buy compute separate from storage? I just want to buy more compute, or I just want to buy more storage I want to virtualize Hadoop like AWS does… How can I increase my utilization? I want to share storage among server nodes with an NAS (i.e. Isilon) I need to lower my infrastructure costs Compared to status quo… Compared to NAS… Compared to Cloud…

Benefits of Software Defined Infrastructure to Hadoop Operators
# Benefit Details 1 Lower Capital Costs Reduced server costs (don’t have to buy disks) Buy Less Storage (higher utilization) Less Rack space needed (more dense CPU and Storage) Lower Disk cost (3.5” disks cost less than 2.5”) 2 Lower Operational Cost Add storage without physical labor Replace failed drives without labor Less equipment = reduced power 3 Speed up Big Data Deployments Faster Time to Value Create new clusters and nodes in minutes instead of weeks Integrated with Cloudera Director and Horton CloudBreak Share resources among multiple applications and clusters The benefits of this system are obvious. You save significantly in capital expenditure when you look at a 5 year horizon of server+disk purchases. Today, when you refresh your servers, you have to move all the data in them off to some other location, which is a time consuming process. With the DriveScale system, this is no longer necessary. You have all the levers and knobs you need to move resources to the workloads. Underutilized resources can be taken away from clusters and moved elsewhere. This also therefore means that you need less total hardware than if you were building discrete silos of equipment. That results in costs savings, rack space savings and power savings. Finally, the tools we provide help you provision clusters in minutes instead of days.

(Direct Attached Storage)
Benefits of Software Defined Infrastructure $ Lower Server Costs - Example before with DriveScale 2RU Server with DAS (Direct Attached Storage) 1RU diskless Server System/CPU/MoBo/NICs RAM DISK $3,065 $1,142 $12,832 $2,063 $1,246 $172 System/CPU/MoBo/NICs RAM DISK (1TB for OS) 79.5% savings + + TOTAL $17,039 $3,481 TOTAL Save $13,558 per server node. x 1000 nodes = $13M 30

Benefits of Software Defined Infrastructure $ Lower Disk Costs - Example
before with DriveScale 2.5” drive DAS 3.5” drive in JBOD 48% savings $ per 2.5” drive = $0.45/GB $ per 3.5” drive = $0.22/GB Save $385 per drive. Save $192,000 per PetaByte Also note, the sweet spot for 3.5” drives is 8TB. Dell sells these for their JBOD for $794.40, which is only $0.09/GB, (8TB 7.2K RPM NLSAS 12Gbps 512e 3.5in Hot-plug Hard Drive [$794.40]) 31 31

Per Rack Cost; Commodity Server MFG with and without DriveScale (higher drive utilization)
with DriveScale 2 x 48 port 10GbE switches (ON-4940S) 2 x 48 port 10GbE switches (ON-4940S) 43% Savings 20 x R730 2RU Servers w/20 2/5” 1.2TB drives each 20 x R430 1RU Servers $346,410 Day 1 Cost (only 2 JBODs) $198,504 Open RackSpace for more servers 2 x DriveScale Adaptor 2 x JBOD w/60 3.5” 2.0TB drives each 240 Terabytes Storage

Per Rack Cost; Commodity Server MFG with and without DriveScale (equal storage)
with DriveScale 2 x 48 port 10GbE switches (ON-4940S) 2 x 48 port 10GbE switches (ON-4940S) 20 x R730 2RU Servers w/20 2/5” 1.2TB drives each 20 x R430 1RU Servers $692,820 4-year Cost (server refresh) $452,208 35% Savings 4 x DriveScale Adaptor 4 x JBOD w/60 3.5” 2.0TB drives each

DriveScale Deployment Scenarios Preserve your existing investment
Greenfield Start off on the right foot with DriveScale Software Defined Infrastructure Existing Hadoop – Need more storage on nodes (i.e storage bound) Just add a JBOD and a DSA, and add disks to existing servers Existing Hadoop – Rebalance Storage between Nodes and Clusters Just add a JBOD and a DSA, pull unused disks from DAS servers to JBOD, and redistribute storage and nodes into new clusters. Existing Hadoop – Need more compute nodes (i.e. compute bound) Add a compute server, JBOD, and DSA. And you can add more nodes to your cluster without buying more DAS. Existing Hadoop – Moving from Public to Private or Hybrid Cloud Cloudera Director and HortonWorks CloudBreak deployed you to the cloud? They are both integrated with DriveScale to deploy your private cloud the same way. You can even have a single cluster span your private and public clouds (i.e. hybrid).

DriveScale Solution Components

DriveScale Components shown in typical rack deployment
Cloud Software Hardware Top of Rack Switches: port 10GbE (Cisco, Arista, HPE, Dell, Quanta, etc) DSC DriveScale Central 1 per customer Customer Support Remote upgrade Remote licensing DMS DriveScale Management System 1-3 per customer Linux RPM Runs on VMs Inventory Cluster config Node config Out-of-band Management Switch: 1GbE Node Server Compute Pool Rack Servers: 2U, 1U, 1/2U or 1/4U Dell, HPE, Cisco, SuperMicro, Quanta, Foxconn, etc DriveScale Adapter Ethernet to SAS Bridge 1 per JBOD DSN DriveScale Server Node Agent 1 on each node Inventory discovery Storage Pool – JBOD (Just a Bunch of Drives) Dell, HPE, SuperMicro, Sanmina, Quanta, Foxconn, etc 36

The DriveScale System DriveScale Adapter (DSA)
Highly Automated Infrastructure Provisioning and Management DriveScale Adapter (DSA) DriveScale Agent DriveScale Management System (DMS) DriveScale Cloud Central (DSC) DriveScale Agent DriveScale Server Node (DSN)

DriveScale Software Management Architecture
The 4 principal Components: 1. DriveScale Management Server (DMS) Data repository consists of: Inventory (DMS’s, DS Adapters, Switches, JBOD Chassis, Disks, Server Nodes) Configuration (Node Templates, Cluster Templates, Configured Clusters) Typical deployment consists of 3 DMS Systems DMS Database is used as a message bus to communicate with the end points 2. DriveScale Adapter (DSA) DSA Agent Discovery provides inventory for hardware Creates mappings for Server Nodes to consume disks 3. DriveScale Server Node (DSN) DS Server Agent provides inventory for server hardware Consumes mapped disks via DSA 4. DriveScale Central (DSC) Cloud-based Portal where DriveScale repos are stored for software distribution to subscribers.

The DriveScale Adapter
Enables SAS connected drives to be mounted over Ethernet 4 DriveScale Ethernet to SAS Adapters in 1u Chassis Dual Redundant Power Supplies With 80 Gb throughput, a single chassis can comfortably support simultaneous access to 80 drives w/ equivalent performance to Direct Attached Storage 2x 10GbE Interfaces per Adapter 2x 12Gb 4 Lane SAS Interfaces per Adapter The DriveScale system that performs the first part of the magic trick, is a 1U hardware appliance that contains four distinct and separate adapters that connect to servers on one side via 10GbE Ethernet ports, and to JBOD’s on the other side with standard SAS ports. The hardware is designed to be highly resilient with dual power supplies and a passive backplane. The adapters have out of band management ports as well and can comfortably support simultaneous access to 80 drives in JBODs with equivalent performance to direct-attached storage.

PoC Minimal HW Requirements (with resiliency)
1x DSA chassis (includes 4x DS Adapters) 1x JBOD to be loaded with the 60 drives 3 (minimum) x Servers (Data Nodes) with 1 direct attached drive each (4-12 drives will be remotely attached via DSA) 1x Server (Name Node) with 1 direct attached drive 1x Server (can be VM) for DMS and Ambari 2x 10GE switches for Data Flow Management Switch (1GE) DriveScale Proprietary Information © 2017

DriveScale Reference Customers

DriveScale Target Customers
Big Data Applications – Hadoop, Spark, Cassandra, NoSQL, etc On-Premise Applications – Private and Hybrid Public Cloud Concerned about infrastructure costs & wasted spend Application profiles are dynamic – where infrastructure requirements are hard to predict and always changing Approaching Power / Rack Space Limits Want to share infrastructure across multiple applications Want to buy storage and compute independently (storage or compute bound)

DriveScale Target Customers – Common questions/statements
How can I buy compute separate from storage? I just want to buy more compute, or I just want to buy more storage I want to virtualize Hadoop like AWS does… How can I increase my utilization? I want to share storage among server nodes with an NAS (i.e. Isilon) I need to lower my infrastructure costs Compared to status quo… Compared to NAS… Compared to Cloud…

DriveScale Customer Reference - Clearsense
Technology company which transforms data from electronic medical records (EMRs), supporting source systems, & legacy databases to enable new revenue streams for healthcare Real-time analytics solution ingests many kinds of data from existing EMRs & data warehouses, and puts them into one platform to serve up analytics After 18 months in production at AWS, they were unhappy with responsiveness and costs, and decided to build a Private Cloud Hadoop responsiveness is something that they tout to their customers, and this technology supports that via infrastructure flexibility DriveScale Confidential Information © 2016

DriveScale Customer Reference - Clearsense
“DriveScale helps us build a ‘future-proof’ infrastructure. As our customer needs increase, we can now respond more quickly and without having to make massive capital expenditures.” “We spent a lot of time engineering our software environment around real-time needs, ensuring a productive, scalable and economically viable environment. Now, with DriveScale, we have the ability to do the same thing with hardware,” Charles Boicey, Chief Innovation Officer at ClearSense DriveScale Confidential Information © 2016

DriveScale Customer Case Study: AppNexus
Global technology company whose private cloud-based software platform enables and optimizes programmatic online advertising Serves 10s of billions ad buys per day; revenue > $2B annually 2200 Nodes split between Hadoop & Hbase Their customers are asking for more vcore and memory, but not more storage – they wanted to scale compute independent of storage Issues we solve: Highly Compute-bound environment – purchase compute-only nodes Want to reduce Operational impact of refreshing their servers Rack / Power constraints also an issue for them DriveScale Confidential Information © 2016

Timothy Smith, SVP of Technical Operations at AppNexus
"DriveScale understood one of our core requirements, namely the desire to manage CPU and storage resources as separate pools.” “Storage and server technology upgrades move on two different timetables. Without DriveScale, we are forced into storage refresh cycles that aren't strictly necessary, and are very cumbersome. Separating storage from compute allows us to upgrade or reallocate compute resources independent of storage. “ “DriveScale significantly decreases the operations workload, as a result increasing the velocity of delivering new products and features to our customer base. DriveScale will also help us reduce wasted resources trapped in siloed clusters and thus contribute directly to our bottom line." DriveScale Confidential Information © 2016

How Hadoop is different from other enterprise workloads
Hadoop architecture has evolved out of the need to transact and analyze extremely large volumes of data at scale in institutions like Google, Yahoo and Facebook Traditional Enterprise architecture solutions could not satisfy these needs Inability to meet scalability requirements Included redundant software stack which was unsuitable for these applications Expensive Hadoop architecture is based on networked commodity servers with Direct-Attached Storage Commodity storage is less than 1/10th the cost of shared enterprise storage Proven scalability to 1000s of nodes and 100s of PBs of storage Compute task is sent to the server where the data resides (data locality) Resiliency is managed by the software Hadoop includes YARN which creates containers to run jobs (achieves a higher level of utilization than VM’s) ©2017 DriveScale Inc. All Rights Reserved.

Cost is DriveScale’s advantage over other storage solutions
When a disk drive is located in a JBOD, customer only pays for the price of the drive itself When the same disk drive is located in a storage appliance or server, customer pays for the drive AND all the software and storage services that are built into the appliance The average cost of disk drive storage is $0.08/GB This goes up to $0.50 to $1.00 /GB when the same drive is in a storage appliance (EMC Isilon, Datera, Nutanix, etc)

Hadoop Storage Options
3 Good 2 Fair 1 Poor Storage Type DAS (Direct Attached Storage) DriveScale SW Defined Infrastructure Centralized storage NAS or SAN Comments Cost 5-10x Buy disks, instead of proprietary ‘appliances’, which are 5x to 10x the cost. Don’t waste money on storage features Hadoop doesn’t need (dedup, RAID, erasure encoding, etc.) Performance 1/2 - 1/4 Give Hadoop nodes direct access to rack local disks, not shared or centralized file systems with limited IO Bandwidth Utilization 30-50% Buy only the disks you need, pooled local to the rack. This allows better storage utilization and re-balancing disks across nodes in a cluster gives better CPU utilization, putting more servers to work. Adaptability (ability to change node storage) none Re-define your server and storage infrastructure as application needs change. Scalability anti-hadoop Give nodes direct access to their own disks. Don’t share file systems (“nothing shared”). 2 3 1 3 3 1 1 3 3 1 3 2 3 3 1

Hadoop Storage Needs vs Various Storage Types
Data Locality Converged compute & storage Replica-tion Extreme Read BW Low Cost Commodity Total Examples DAS & DriveScale SDI ✔ Dell, HP (nonApollo), Cisco, SuperMicro, etc. NAS - Enterprise ✖ Isilon, Netapp, Gluster NAS - HPC Lustre, GPFS SAN/Block - External ? ScaleIO, Ceph, Datera, Cinder, AWS EBS SAN/Block - Hyperconverged Nutanix, ScaleIO, Robin Object AWS S3, Scality, Swift, EMC ECS ✔ ✖ ? Optimal SubOptimal Debatable

DriveScale Vs Isilon Enterprise NAS
Category Isilon DriveScale Price $ $1.00 per GB 1/5 $ $0.15 per GB Storage Performance Peak Bandwidth: 2.4GB/sec per Isilon Node 4x Peak Bandwidth: 10GB/sec per DSA Hadoop/HDFS version compatibility 1-1.5 year delay from latest Hadoop (Isilon FS needs to continuously be made compatible) Always current (sits below HDFS layer) Storage Scale 288 Compute Nodes (144 Isilon Nodes) 4000+ Compute Nodes. Drive Density 6 – 9 Drives per RU 12 – 18 Drives per RU Network Complexity Two Networks (Ethernet and Infiniband) One Network (Ethernet) Compute/Storage Ratio 2:1 (2 Compute nodes per Isilon node) 16:1 (16 Compute nodes per DSA) Minimum config 3 Isilon Nodes 1 JBOD/DSA

Cloudera Do’s and Don’t of building private Infrastructure…
Avoid Isilon for large (>100 servers) Hadoop infrastructure DriveScale behaves just like Locally Attached Storage

DriveScale (DS) vs HPE BDRA
TCO for DriveScale is approximately 38% cheaper just for HPE hardware costs Additional Cloudera/Hortonworks licenses for each of the data nodes in BDRA Apollo 4200 which serves as the data node has a single controller and hence becomes a single point of failure (SPOF) for BDRA - If a node fails the all the 28 drives needs to be replicated on the other storage nodes and drives. Ex: If the drives are 6TB each then, the network is flooded with 168 TB of data at once. - The network is flooded with the data from the 28 drives to be replicated and the ongoing jobs will be delayed and are affected with the data replication - Spare Apollo 4200 data nodes might be required based on the amount of data in each data node. - With DS we have 4 path to each drive in the JBOD with dual IO controllers. - With DS we can sustain with 1 JBOD IO controller, 1 switch and 1DSA failures. 40Gb switches are required for BDRA. 10Gb switches for DS. OS maintenance on data nodes: If there an OS update required, then the entire node with 28 drives needs to be put under maintenance mode and that would also trigger replication of data across the other available storage nodes Firmware upgrades of storage nodes poses a similar issue as above Every read/write in a BDRA cluster is over the network One of the reasons that traditional Hadoop deployments stripe data across a large number of nodes with a few disks each, is that the ‘failure locality’ is more manageable. If a large amount of data is stored on a single node, the penalty for failure is tremendous which is a problem for BDRA. DriveScale Confidential Information © 2017

DriveScale Confidential Information © 2017
DriveScale Summary First Enterprise-Class Architecture designed for Scale Out Infrastructure Eliminate Overprovisioning Lower TCO Improve Agility to workload changes Integrated with Cloudera Director GENE To wrap it all up, We believe there is a big market opportunity here as the industry moves to scale-out. We have the first enterprise architecture designed from the ground up for this new world. And finally, we have a team that knows how to build these kinds of products. DriveScale Confidential Information © 2017

DriveScale’s Core Value Propositions
Save Money Lower Server and Storage CapEx Reduce Time to Value of your Big Data initiative Improve Utilization 3x better utilization of hardware resources Modify Infrastructure on demand to respond to changing workloads Multiple workloads on the same infrastructure Simplify Everything Commodity HW and fewer composable elements Software controlled Infra One-click deployment DriveScale value propositions are: First and foremost, alleviating pain around rigid infrastructure. We provide flexible and responsive physical infrastructure so that admins can provision exactly what’s needed and rebalance resources on demand Second, we provide a solution that is simple to deploy whether small or large - we provide a solution that is functionally and performance-wise equivalent to the standard rack server model, without changes required to an app stack. We provide a comprehensive set of REST APIs to make this system fully automatable, which is table stakes at scale. Finally, this has been designed from the start to be an enterprise grade solution, from high availability throughout the system to investments in security and an acknowledgement that people would want to bring their own server and JBOD hardware to the table.

Frequently Asked Questions
Q1) Can I continue to buy servers and storage from my preferred vendors? A1) Yes. Drivescale does not sell server or storage, or JBODS. But all the big vendors do. The only HW you will need from DriveScale is DSA (DriveScale Adaptor), to convert the JBOD SAS interface to Ethernet. 4 DSA (1RU chassis full) per high performance JBOD. Q2) Does DriveScale SDI Disaggregation work for more than just Hadoop? A2) Yes. It will work for any application. However, there may be performance implications for _non_ Big Data applications that use “small” blocks. Q3) What is the minimum storage required in “diskless” compute servers? A3) Any drive, as small as possible, just for the Operating System. Could be USB or small FLASH. All data stored on JBOD disks. Q4) Does DriveScale support SSD in JBODs? A4) Yes. The JBOD can have many types of heterogenous storage, as long as SAS compatible: HDD, High or low RPM, High or low Capacity, SSD, etc. Q5) Is there a network impact of accessing disks over iscsi/Ethernet vs direct attached SAS? A5) Minimal to zero impact; HDFS storage access peaks during copy of data from one node to another. This requires one node to read its disk (NIC RX port activity from disk) and transfer the data to another node (NIC TX port activity to other node), the receiving node does the inverse. In both nodes, there is an increase in network traffic to read or write the disk, but this increased traffic is on the opposite side of the bidirectional link used for internode traffic, so there is no performance impact. TestDFSIO performance testing results confirm this answer.

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