Enmotus Blog

How Many IOPS Do You Need For Real-World Storage Performance?

Posted by Adam Zagorski on Aug 22, 2017 11:12:17 AM

We hear lots of hype today about millions of IOPS from someone’s latest flash offering. It’s true that these units are very fast, but the devil is in the detail and often using the products yields a much weaker performance than the marketing would lead you to expect. That’s because most vendors measure their performance using highly tweaked benchmark software. With this type of code, the devil is in the details.

A bit extreme, perhaps, but all benchmarks can be tuned for optimal performance, while we never hear about the other, slower, results.

What eats up all of that performance? In the real world, events are not as smoothly sequenced as they are in a benchmark. Data requests are not evenly spread over all the storage drives, nor are they evenly spread in time. In fact, I/O goes where the apps direct, which means some files get much more access, making the drives they are on work hard but leaving other drives nearly idling.

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Topics: NVMe, big data, Data Center, hyperconverged, storage analytics

Storage Analytics and SDS

Posted by Jim O'Reilly on Aug 17, 2017 11:30:36 AM

Software-defined storage (SDS) is a part of the drive to make infrastructure virtual by providing an abstraction of the control logic software (the control plane) from the low-level data management (data plane). In the process, the control plane becomes a virtual instance that can reside in any instance in the computer cluster.

The SDS approach allows the control micro-services to be scaled for increased demand, to be chained for more complex operations (Index+compress+encrypt, for example), while making the systems generally hardware agnostic. No longer is it necessary to buy storage units with a given set of functions only to face a forklift upgrade if new features are needed.

SDS systems are very dynamic, with mashups of micro-services that may survive only for a few blocks of data. This brings new challenges:

  • Data flow - Network VLAN paths are transient, with rerouting continuously happening for new operations, for failure recovery and for load balancing
  • Failure detection - Hard failures are readily detectable, allowing a replacement instance and recovery to occur quickly. Soft failures are the problem. Intermittent errors need to be trapped, analyzed and mitigation exercised
  • Bottlenecks - Slowdowns occur in many different places. Code is not perfect, nor is it 100 percent tested bug-free. In complex storage systems, we’ll see path or device slowdowns, on the storage side, and instance or app issues, on the server side. Moreover, problems may reside in the network caused by collisions both at the endpoints of a VLAN and in the intermediate routing nodes.
  • Everything is virtual - The abstraction of the planes complicates root cause analysis tremendously
  • Automation - There is little human intervention in the operation of SDS. Reconnecting and analyzing manually is naturally very difficult, especially in real-time
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Topics: Data Center, software defined storage, storage analytics, SDS

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