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Storage Design with Flash Storage. One Big LUN vs multiple smaller LUNS.

Traditionally, when storage design was done for VMware Environments, a lot of criteria had to be considered. This included

  • Number of Drives
  • Speed of Drives
  • Number of IOPS per each drive
  • RAID penalty
  • Write Penalty
  • Read Penalty
  • Scalability of the Array

But with the advent of all flash arrays (XIO, Pure, Nimble, Violin etc etc) a lot of these parameter no longer constraint the storage design for VMware environments. Each of the AFA offerings have their own RAID kind of technology, which pretty much  guarantees a very high resiliency to failure and data loss. Also with the new kind of Flash drives introduced (eMLC from memory), the consumer level SSDs are no longer used in AFAs. So now that the physical limitations on the drives have been eradicated, lets look at the next steps.

Queue Depth: 

Queue depth is a very misleading constraint, there are queue depths at each level, LUN, Processor, Array. So each physical enitity(or not so physical for CNAs and LUN) has an individual queue depth. How do we address this short coming? If there is a lot of IO being thrown at the Array, if its not able to process it, the queue is going to fill up.

If the host parameters are not set properly, it will start to fill up the HBA queue depth across the multiple LUNs that it has access to. Some of these parameters can be changed to ensure that the ESXi vmkernel process does things differently when using AFAs.

I’ve previously mentioned some parameters that need to be changed for XtremIO. I guess the same would apply for all the AFAs out there. Using ESXi with AFA and not changing advanced parameters to take advantage of AFA is like, buying a Ferrari to drive in Melbourne CBD. It only proves that you are an idiot, restricted by the ‘speed limit’.

OK But what about LUNs:

Now to the original question, One Big LUN vs Multiple smaller LUNS. Each decision has its own advantages and disadvantages, for example, choosing one Big LUN can give cumulative IOPS available across multiple storage nodes in AFA. So if one node provides 250,000 IOPS (random workload 50% read), then adding another node to it will enhance it to 500,00 IOPS. That single node provides more IOPS than a fully scaled and filled VNX 7500. Thats a lot of horsepower if you ask me.

The same can be said for multiple smaller LUNS, each LUN created is spanned (atleast in XtremIO AFAIK) across all the available nodes in the cluster. So you would still get the benefit of insane amount of IOPS for each decision.

Other Considerations:

There are other considerations that you will need to take into account when designing storage for VMware. To start with, workload consideration is a good one. Depending on the workload thats consuming all of these resources, you might want to provide a single big LUN or the application architecture might force you to use multiple smaller LUNS. One of my customers’ SQL Team is convinced that even on AFA, the data and the log LUN have to be separated on ‘spindles’. I explained about the lack of spindles and the redundancy/resiliency/availability aspect of AFA. After a long discussion, it was agreed that there would still be multiple LUNs created but all of them on the same 2 XIO node array. Not across the other 2 x 2 node XIO arrays that are available.

What about DR/SRM:

DR/SRM strategy doesn’t need to change significantly for SRM. I have always believed in providing the optimal number of LUNs for SRM for a mixed workload. Some applications might require a separate LUN (for a vApp for example). While some are happy to co-exist. It also comes down to the application owners, some application owners are adamant that the workloads should be maintained seperately, while others are happy to co-exist on the same LUN as long as their RTO/RPO requirements are met.

So in short, the answer is ” IT DEPENDS“. But my vote goes to multiple medium sized LUNs (10-12TB) :). This will provide the advantages of both big and small LUNS.

Whats your say ?

I’d appreciate the comments about this in the blog rather than on twitter, but then again both are social media so doesn’t matter.

Koodzo

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