Tidal scale short_story_v2
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1) Traditional server architectures struggle to handle growing data volumes and values that decline rapidly over time. In-memory computing is needed for performance but current approaches like sharding data reduce accuracy. 2) Software-defined servers address these issues by allowing servers to expand in size on demand using standard hardware. This provides in-memory performance at large scale with self-optimizing servers that require no changes to applications or operating systems. 3) TidalScale uses machine learning to transparently optimize resource allocation across multiple physical servers operating as a single large virtual machine. This provides up to 200x faster performance than a single server for memory-intensive workloads like machine learning.
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Tidal scale short_story_v2
- 1. Introduction to Software-Defined Servers Flexible – Fast - Easy
- 2. The Problem Variety & Volume: Data growing at 62% CAGR GB TB PB EB ZB DataVolumes Time Velocity: Data value declines with age BusinessValue Age of Data (seconds) Data Growth is HUGE The Value of Data Is Declining Rapidly
- 3. NVMe Flash 150 μs $150,000 Flash Array 1 ms $1,500,000 Hard Drive 5 ms $7,500,000 TCP packet retransmit 2 s $2,000,000,000 The Problem In-Memory Computing is Key Operation Processing Latency In Human Terms L1-L3 Cache 1-13 ns $1 DRAM 50 ns $50 In-Memory Computing Is Needed For Performance
- 4. Current Approaches - Lower prediction accuracy Algorithm - Lower prediction accuracy Sub Sample Do You Sample the Data?
- 5. Current Approaches Shard 4 Shard 3 Shard 2 Shard 1 - Lower prediction accuracy - Time & Money Or, Do You Shard the Data?
- 6. What do you do? Not having the right Server adds Complexity, increases Risks, and misses Opportunities
- 7. What If Servers were Software-Defined? Are your servers too small?
- 8. What If Servers were Software-Defined? What if your Servers could expand to fit all of your data?
- 9. Software-Defined Servers • Just the right size • In-memory performance at scale • As many cores as needed • Self optimizing • Everything just works • Uses standard hardware Software-Defined Servers What if you could choose the size of your servers? Just the right size. In-memory performance. Self-optimizing. Everything just works. Using Commodity Servers.
- 10. Software-Defined Servers – the Missing Piece Software-Defined Datacenters Storage Manage Network Server Software-Defined Servers are the missing piece in the Software- Defined Datacenter
- 11. Traditional VirtualizationVirtualPhysical Multiple virtual machines share a single physical server Virtual Machine Virtual Machine Virtual Machine Application Operating System 100%,bit-for-bit unmodified Application Operating System Application Operating System Traditional virtualization cuts a server into smaller virtual servers (VM’s)
- 12. TidalScale: Software-Defined Servers Software-Defined Servers are the inverse. One, two, ten or a full-rack of servers operating as one! Single virtual machine spans multiple physical servers Application OperatingSystem … HyperKernel HyperKernel HyperKernel TidalScale Virtual Machine 100%,bit-for-bit unmodified
- 13. Machine Learning Driven Self-Optimization HyperKernel … HyperKernel HyperKernel HyperKernel HyperKernel Uses patented machine learning to transparently align resources Application Operating System TidalScale Software-Defined Server And it performs. Machine learning keeps locality by constantly & transparently optimizing.
- 14. 100% Compatible Applications Operating Systems Virtual Machine If it runs, it runs on a TidalScale Software-Defined Server TidalScale Software-Defined Server HyperKernel … HyperKernel HyperKernel HyperKernel HyperKernel Containers Applications and Operating Systems just work. No Changes. Containers work great, too!
- 15. Benchmark: Open Source R on TidalScale https://github.com/TidalScale/R_benchmark_test Open Source R Performance Comparisons TotalExecutionTime(Minutes) 100 0 100,000 200,000 400,000 700,000 - 200 300 400 500 600 300,000 500,000 600,000 Bare Metal Server (128GB) 158 days TidalScale Software-Defined Server (5 x 128GB nodes) 1,325 3,787 W orkload Size in GB A performance example. Avoid the memory cliff and scale the server to match the size of the data. 200X Faster
- 16. Tomorrow’s Servers Today: A Game-Changer “Software-defined Servers make it easy to run memory-intensive applications like data mining, machine learning and simulation.” Marc Jones, Director & Distinguished Engineer, IBM Are you interesting in having the server of 2025?
- 17. “This is the way all servers will be built in the future.” Gordon Bell Industry legend & 1st outside investor in TidalScale
Notes de l'éditeur
- Slide 1: Software-Defined Servers: A Game Changer for Data Scientists | Hi I'm Gary Smerdon, CEO of TidalScale. It's my pleasure to be here today. "Game Changer" is a powerful word conjuring the likes of the iPhone and the Internet. But Software-Defined Servers are a game changer for Data Scientists and I'm here today to share why.
- Everyone here is intimately familiar with the problem. Data *Volume* and *Variety* is growing explosively at 62% a year. Simultaneously, the *Velocity* of data change is increasing rapidly such that the business value of that data decreases exponentially by its age in seconds. Getting a handle on this requires that computing be done *in-memory* because the alternative, paging from storage, is so incredibly slow. In dollar terms its the difference between buying your weekly groceries for $50 versus for $150,000. Such outsize costs would have a big negative impact on your annual food budget! We call this latency difference the *Memory Cliff* and it has a similar negative impact on application performance.
- Everyone here is intimately familiar with the problem. Data *Volume* and *Variety* is growing explosively at 62% a year. Simultaneously, the *Velocity* of data change is increasing rapidly such that the business value of that data decreases exponentially by its age in seconds. Getting a handle on this requires that computing be done *in-memory* because the alternative, paging from storage, is so incredibly slow. In dollar terms its the difference between buying your weekly groceries for $50 versus for $150,000. Such outsize costs would have a big negative impact on your annual food budget! We call this latency difference the *Memory Cliff* and it has a similar negative impact on application performance.
- You are all familiar with the solutions available for managing large data problems today. 1. Take a sample of the data, 2. Use a clever algorithm that makes some compressed representation of the data space, or 3. Shard the data set to fit in the memory of multiple small servers. All of these approaches have the disadvantages of lowering prediction accuracy, requiring time and effort and generally just getting in the way of doing data science. It's like having to do the plumbing when you just want to cook dinner.
- You are all familiar with the solutions available for managing large data problems today. 1. Take a sample of the data, 2. Use a clever algorithm that makes some compressed representation of the data space, or 3. Shard the data set to fit in the memory of multiple small servers. All of these approaches have the disadvantages of lowering prediction accuracy, requiring time and effort and generally just getting in the way of doing data science. It's like having to do the plumbing when you just want to cook dinner.
- What if the servers you used for data science were themselves software-defined? You could simply do your data science cookery without having to worry about the plumbing! To continue the thought experiment, imagine these servers could be assembled from ordinary servers, tuned themselves automagically, delivered in-memory performance at any scale and didn't require any change to software applications or operating systems. If such a software-defined server existed, it would deliver a missing piece in today's data centers where virtually _every_ other component _is_ software-defined. Today I am happy to share with you that such a technology _does_ exist: *TidalScale*. But to explain it I have to back up a bit and explain Traditional Virtualization.
- What if the servers you used for data science were themselves software-defined? You could simply do your data science cookery without having to worry about the plumbing! To continue the thought experiment, imagine these servers could be assembled from ordinary servers, tuned themselves automagically, delivered in-memory performance at any scale and didn't require any change to software applications or operating systems. If such a software-defined server existed, it would deliver a missing piece in today's data centers where virtually _every_ other component _is_ software-defined. Today I am happy to share with you that such a technology _does_ exist: *TidalScale*. But to explain it I have to back up a bit and explain Traditional Virtualization.
- What if the servers you used for data science were themselves software-defined? You could simply do your data science cookery without having to worry about the plumbing! To continue the thought experiment, imagine these servers could be assembled from ordinary servers, tuned themselves automagically, delivered in-memory performance at any scale and didn't require any change to software applications or operating systems. If such a software-defined server existed, it would deliver a missing piece in today's data centers where virtually _every_ other component _is_ software-defined. Today I am happy to share with you that such a technology _does_ exist: *TidalScale*. But to explain it I have to back up a bit and explain Traditional Virtualization.
- What if the servers you used for data science were themselves software-defined? You could simply do your data science cookery without having to worry about the plumbing! To continue the thought experiment, imagine these servers could be assembled from ordinary servers, tuned themselves automagically, delivered in-memory performance at any scale and didn't require any change to software applications or operating systems. If such a software-defined server existed, it would deliver a missing piece in today's data centers where virtually _every_ other component _is_ software-defined. Today I am happy to share with you that such a technology _does_ exist: *TidalScale*. But to explain it I have to back up a bit and explain Traditional Virtualization.
- Traditional virtualization effectively divvies-up a server into multiple virtual servers. The key fact is that the virtual servers have _no idea_ they aren't actually running on hardware. The applications and operating system is unmodified.
- TidalScale uses virtualization but does it _across_ multiple physical servers, effectively allowing you to treat physical servers like lego building blocks.
- Under the covers, TidalScale uses machine learning to automatically co-locate compute entity with the resources it needs. And just like ordinary virtualization it does all of this transparently - the OS and applications have no idea they aren't running on hardware....
- TidalScale can run virtually everything unchanged, and we deliver in-memory performance, for example...
- To document our scalability we wrote and published a benchmark on Open Source R. It measures the time to execute load, join and then the 5 most commonly used data science algorithms on CMS insurance claim data. We ran this benchmakr on a bare metal 128GB server and then on a TidalScale TidalPod composed of five 128GB servers. As you can see on this chart, the Software-defined Server's performance scales linearly.
- So how would it change the way you tackle data science problems to have the ability to deploy the hardware systems of tomorrow today? An example of the kind of systems we can create is the 15TB 400 core TidalPod we deployed at SoftLayer this past June using 20 standard servers. Marc Jones captured it best: “Software-defined Servers make it easy to run memory-intensive applications like data mining, machine learning and simulation.”
- TidalScale Software-Defined Servers are a game changer! They let you explore your data more easily, improve your results and allow you spend more time doing data science instead of data plumbing all while lowering your operational TCO and increasing IT flexibility. This is why one of our early investor's said it best: "This is the way all servers will be built in the future."