Enmotus Blog

Using advanced analytics to admin a storage pool

Posted by Adam Zagorski on Sep 25, 2017 1:43:50 PM

Manual administration of a virtualized storage pool is impossible. The pace of change and the complexity of the information returned from any metrication is too complex for a human to understand and respond in anything close to an acceptable timeframe.

Storage analytics sort through the metrics from the storage pool and distil useful information from a tremendous amount of near-real-time data. The aim of the analytics is to present information about a resolvable issue in a form that is easy to understand, uncluttered by extraneous data on non-important events.

Let’s take detecting a failed drive as an example. In the early days of storage, understanding a drive failure involved a whole series of CLI steps to get to the drive and read status data in chunks. This was often complicated by the drive being in a RAID array drive-set. This approach worked for the 24 drives on your server, but what happens when we have 256 drives and 10 RAID boxes, or 100 RAID boxes…get the problem?

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Topics: NVMe, big data, All Flash Array, Data Center, data analytics, cloud storage

Car Wrecks and Crashing Computers

Posted by Jim O'Reilly on Sep 13, 2017 12:05:33 PM

We are just starting the self-driving car era. It’s a logical follow-on to having GPS and always-connected vehicles, but we are still in the early days of evolution. Even so, it’s a fair bet that a decade from now, most, if not all, vehicles will have self-driving capability.

What isn’t clear is what it will look like. Getting from point A to point B is easy enough (GPS), and avoiding hitting anything else seems to be in the bag, too. What isn’t figured is how to stop those awful traffic jams. I live in Los Angeles and a 3-hour commute Friday afternoon is commonplace. In fact, Angelinos typically spend between 6 and 20 hours a week in their cars, with the engine running, gas being guzzled and their tempers being frayed!

It’s particularly true in LA that each car usually has a single occupant, so that’s a lot of gas, metal and pavement space for a small payload. What this leads us to is the idea of

  1. Automating car control and centralizing routing. This would allow, via a cloud app, load-balancing the roads and routing around slowdowns
  2. Making the vehicles single or dual seater electric mini-cars
  3. Using the Mini-cars to pack more effective lanes and move cars closer together
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Topics: big data, data analytics, cloud storage

Automating Storage Performance in Hybrid and Private Clouds

Posted by Jim O'Reilly on Jun 27, 2017 10:10:00 AM

Reading current blogs on clouds and storage it’s impossible not to conclude that most cloud users have abandoned hope on tuning system performance and are just ignoring the topic. The reality is that our cloud models struggle with performance issues. For example, a server can hold roughly 1000 virtual machines.

With an SSD giving 40K IOPS, that’s just 40 IOPS per VM. This is on the low side for many use cases, but now let’s move to Docker containers, using the next generation of server. The compute power and, more importantly, DRAM space increased to match the 4,000 containers in the system, but IOPS dropped to just 10/container.

Now this is the best that we can get with typical instances. One local instance drive and all the rest is networked I/O. The problem is that network storage is also pooled and this limits storage avail

ability to any instance. The numbers are not brilliant!

We see potential bottlenecks everywhere. Data can be halfway across a datacenter instead of localized to a rack where compute instances are accessing it. Ideally, the data is local (possible with a hyper-converged architecture) so that it avoids crossing multiple switches and routers. This may be impossible to achieve, especially if diverse datasets are being used for an app.

Networks choke and that is true of VLANs used in cloud clusters. The problem with container-based systems is that the instances and VLANs involved are often closed down by the time you get a notification. That’s the downside of agility!

Apps choke, too, and microservices likewise. The fact that these often only exist for short periods makes debug both a glorious challenge and very frustrating. Being able to understand why a given node or instance runs slower than the rest in a pack can fix a hidden bottleneck that slows completion of the whole job stream.

Hybrid clouds add a new complexity. Typically, these are heterogeneous. The cloud stack in the private segment likely is OpenStack though Azure Stack promises to be an alternative. The public cloud will be one of AWS, Azure or Google, most likely. This means two separate environments, very different from each other in operation, syntax and billing, and an interface between the two.

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Topics: Data Center, data analytics, cloud storage

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