Webinar: Does Your Data Center Need NVMe?
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The document discusses the relevance and implementation challenges of NVMe (Non-Volatile Memory Express) in data centers, highlighting its advantages such as lower latency and high scalability, but also noting drawbacks like higher costs and the need for infrastructure overhaul. It emphasizes the importance of production workload modeling to assess NVMe's performance and compatibility with existing systems, and the necessity of thorough testing to ensure optimal deployment. Additionally, it advises that performance bottlenecks may arise from various factors beyond storage solutions, advocating for a comprehensive analysis of workloads to achieve effective performance improvements.
Webinar: Does Your Data Center Need NVMe?
- 1. Does Your Data Center Need NVMe? For audio playback and Q&A go to: bit.ly/DCNeedNVMe
- 2. OurSpeakers Henry He, Director of Product Management George Crump, Founder and Lead Analyst Mitul Patel, Sr. Systems / Software Engineer
- 3. All About NVMe ● What is NVMe? ● How is NVMe coming to market? ○ NVMe flash drives ○ NVMe flash systems ○ NVMe over Fabrics
- 4. What is NVMe’s Appeal? ● Lower Latency ● PCI Interconnect ● Networkable (shared storage)
- 5. What are the NVMe Drawbacks? ● More expensive, Eco-system needs to be better ● Not an upgrade, a new storage system ● Infrastructure overhaul (for end-to-end) solutions
- 6. 5 Expectation for NVMe is sky high 5 92% have no direct experience with NVMe 91%+ expect positive impact from NVMe 68%~84% intend to deploy NVMe technologies Source: State of NVMe: Perceptions and Misconceptions, ActualTech Media,
- 7. What Attributes do NVMe ready Workloads Have? ● Massively parallel ● High bandwidth ● Application scalability ● Rapid response time
- 8. IT Needs ○ To make sure that its applications demonstrably benefit from NVMe ○ To make sure infrastructure sustains NVMe IO capabilities ○ Performance claims from vendors vary wildly Testing New Systems is Critical
- 9. Building a Test Environment is Hard! ● Requires significant compute investment ● Requires networking better than or least as good as production ● Test or Scripts to sustain continuous IO stream ● Investment in the lab may be bigger than production
- 10. Production Workload Modeling May be The Only Practical Way to Test ● What is Production Workload Modeling? ● Production Workload Modeling architecture ● Production Workload Modeling process
- 11. Leads to The Creation of a Storage Performance Validation Practice ● A formal testing and evaluation process ● Continuous testing of the current environment to predict next required upgrade
- 12. 11 A few things to remember when you’re trying to improve performance Performance is a function of your workloads Your performance bottlenecks might be elsewhere (for once, storage is not to blame!) Problems can come from the least expected places
- 13. 12 A few things to remember when you’re trying to improve performance Performance is a function of your workload Your performance bottlenecks might be elsewhere (for once, storage is not to blame!) Problems can come from the least expected places You don’t want to be this guy (and, we’ve seen a lot of guys like him…here are a few examples)
- 14. 13 A system that performs well for one workload might not perform well for another workload Enterprise-grade software features, like inline compression and deduplication, can also affect performance in unexpected ways A few things to remember when you’re trying to improve performance 1. Performance is a function of the workload IOPS 0 50000 100000 150000 200000 250000 300000 20% / 80% 50% / 50% 80% / 20% 20% / 80% 50% / 50% 80% / 20% 20% / 80% 50% / 50% 80% / 20% Vendor A Vendor B 20% Reducible 50% Reducible 80% Reducible Inline Dedupe / Compression: Configuration A does better when data is highly reducible Configuration A Configuration B Read / Write ratios: Performance gap is greatest when workloads are read heavy Read / Write ratios
- 15. 14 Your applications, especially your most critical applications, do not live alone in a silo. A few things to remember when you’re trying to improve performance 2. Your performance bottlenecks may be elsewhere A 2x cluster deployment resulted in virtually zero performance gains Internal VM to VM communication VM to Datastore dependency VM communication inside and outside the DC Moving an application to NVMe would not have made a noticeable performance to the end user.
- 16. 15 Many planned and unplanned changes can affect performance: - Introducing new technologies - Changing user behaviors - VMotion sickness - Even innocent firmware upgrades! A few things to remember when you’re trying to improve performance 3. Problems can come from the least expected places • Completely ended surprises with new firmware releases “Before VI, our latest storage upgrade would have been an all- hands- on-deck call-out and my team would have been severely criticized for what could have been interpreted as a real problem. “ “With VI monitoring, we could demonstrate, in real- time, that longer latencies were due to the upgrade and not by any real problems in the SAN” Response Time Application response time would have increased by 3x after the firmware “upgrade”!
- 17. 16 Production Workload Modeling Methodology for NVMe-oF 1. Analyze your own production workload data Production Workloads Analysis Application Workloads Storage Infrastructure Analyze Commands Temporality Locality Data Production Workloads Models NVMe Storage Application Workload Models SANBlaze Workload Generator NVMe-oF Model Commands Temporality Locality Data Make decision based on your data Continuously monitor your decision
- 18. 17 Create clustering of Workloads - In production, may have 100,000+ ITLs - In test environment, may not have, and not necessary, to replicate that 1:1 - Clustering provides a highly accurate yet scalable way of modeling workloads Production Workload Modeling Methodology for NVMe-oF Test with a representative test environment Production Workload Clustering Algorithm WL WL WL WL Composite Workload NVMe-oF System Under Test Application Workloads Storage Infrastructure 95% ~ 99% accuracy
- 19. 18 Industry unique Production Workload Analysis and Modeling solution: • Analyze and model your current production storage workloads • Determine optimal NVMe-oF storage systems and configurations • Contain CAPEX costs • Make your NVMe decision with your data Workload Modeling Platform Workload Generator+
- 20. 19 Broad storage infrastructures coverage: • NVMe-oF Storage Arrays • NVMe-oF Storage Infrastructures • Current SAN, NAS, Object infrastructures • Speeds: 16G – 32G FC, 10G - 100G Ethernet • Custom server-SANs: NVMe drives, hosts, NICs • Advanced use cases: error handling, RAID, etc. Workload Modeling Platform Workload Generator+
- 21. 20 Production Workload Modeling Methodology for NVMe-oF Purpose-built workload modeling solution for current and future infrastructures Simple to use sliders that enable complex workloads with ease! Optimize the NVMe queues to maximize performance for your environment Understand clearly what is best for your own workloads
- 22. 21 Purpose-built Test Solution vs. DIY Test Labs Do MORE testing. Do it FASTER. Cover more USE CASES. Lower Testing Costs! Workload Generator 1U footprint Realism, Repeatability, Scalability >42U + VM, DB and OS Licenses Synthetic, Tribal CAPEX & OPEX
- 23. Thank you! www.storageswiss.com georgeacrump@storageswiss.com Twitter: twitter.com/storageswiss YouTube: www.youtube.com/user/storageswiss www.virtualinstruments.com sales@virtualinstruments.com Twitter: twitter.com/Virtual_Inst YouTube: www.youtube.com/user/sos4sans www.sanblaze.com info@sanblaze.com Twitter: twitter.com/sanblaze_tech YouTube: www.youtube.com/user/SANBlazeVideos
- 24. Does Your Data Center Need NVMe? For complete audio and Q&A please register for the On Demand Version: bit.ly/DCNeedNVMe
Editor's Notes
- #15: Often the performance conversation of NVMe goes something like “SAS / SATA with SCSI performs at X, all else being equal, NVMe performs at Y. So NVMe gives you N% improvement.” Well, that’s technically correct, but, the “all else being equal” is a very big statement that generally can’t be applied in real life, because your workloads are constantly different. Here we have an example where you can’t simply draw a line in the sand and say A is better than B, because workload behaviors change all the time. Here we have two configurations, where Configuration B performs a lot better than A when workloads are read heavy, and when data is not highly reducible. But as the workloads become more and more reducible, we see that Configuration A starts to outperform Configuration B. So what you’re seeing here is that enterprise-grade software features like inline compression and deduplication will also affect overall workload performance. So, if you’re only looking at NVMe from the perspective of performance for a particular “sample workload”, you may be unpleasantly surprised when you actually deploy it in your environment.
- #16: This piece is actually pretty interesting. Not too long ago we went in to profile a particular production environment, and graphed out the communication and storage dependency map of a segment of the data centers. So what you’re seeing is that there are a set of hosts that communicate with each other very frequently, and these hosts are spread across a number of datastores. In addition, many hosts communicate within the intranet as well as the internet. So, if you’re focused on your tier 0 apps let’s say, and you’re focused on upgrading their storage to NVMe, then you might not get the end performance boost that you expect for your end users because these tier 0 apps may be waiting on other apps and hosts that are running on slower storage. Or, depending on the application, it may spend a lot of its time going over the internet getting data from SaaS services or apps you moved to the cloud.
- #17: Now this last one has nothing to do with NVMe directly. But as with all new technologies, you should expect more frequent software updates and patches. Even the most innocent changes like a firmware “upgrade”, may turn out to be the worst decision you’ve ever made.