Tag Archives: resiliency

PART 1 – Problems with RAID and Object Based Storage for data protection

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I regularly get asked to compare the resiliency of traditional centralized storage with converged as well as newer technologies such as hyper-converged.

So this post will discuss the problems with RAID and newer hyper-converged solutions using Object based storage for data protection.

This post will discuss two examples below, with Part 2 discussing Hyper-converged solutions using Distributed File Systems.

1. Traditional RAID

2. Hyper-converged Object Based Storage

Starting with Traditional shared storage, and the most common RAID level in my experience, RAID 5.

The below diagram shows a 3 x 4TB SATA drives in a RAID 5 with a Hot Spare.
3 Disk R5 w Hot Spare NO BG

Now lets look a drive failure scenario. We now have the Hot Spare activate and start rebuilding as shown below.

3 Disk R5 w Hot Spare REBUILDING NO BG

So this all sounds fine, we’ve had a drive failure, and a spare drive has automatically taken its place and started rebuilding the data.

The problem now is that even in this simplified/small example we have 2 drives (or say 200 IOPS of drives) trying to rebuild onto just a single drive. So the maximum rate at which the RAID 5 can restore resiliency is limited to that of a single drive or 100 IOPS.

If this was a 8 disk RAID 5, we would have 7 drives (or 700 IOPS) trying to rebuild again to only a single drive or 100 IOPS.

There are multiple issues with this architecture.

  1. The restoration of resiliency of the entire RAID is constrained by the destination drive, in this case a SATA drive which can sustain less than 100 IOPS
  2. A single subsequent HDD failure within the RAID will cause data loss.
  3. The RAID rebuild is a high impact activity on the storage controllers which can impact all storage
  4. The RAID rebuild is an especially high impact activity on the virtual machines running on the RAID.
  5. The larger the RAID or the capacity drives in the RAID, the longer the rebuild takes and the higher the performance impact and chance of subsequent failures leading to data loss.

Now I’m sure most of you understand this concept, and have felt the pain of a RAID rebuild taking many hours or even days, but with new hyper converged technology this issue is no longer a problem, right?

Wrong!

It entirely depends on how data is recovered in the event of a drive failure. Lets look at an example of an hyper-converged solution using an object store.The below shows a simplified example of a Hyper-converged Object Based Storage with 4 objects represented by Object A,B,C and D in Black, and the 2nd replicated copy of the object represented Object A,B,C and D in Purple.

Note: Each object in the Object Store can be hundreds of GB in size.HyperconvergedObjectStoreNormal

Let’s take a look what happens in a disk failure scenario.

HyperconvergedObjectStoreFailure

From the above diagram we can see a drive has failed on Node 1, which means Object A and Object D’s replica have been lost. The object store will then replicate a copy of Object A to Node 4, and a replica of Object D to Node 2 to restore resiliency.

There are multiple issues with this architecture.

  1. Object based storage can lack granularity as Objects can be 200Gb+.
  2. The restoration of resiliency of any single object is constrained by the source drive or node.
  3. The restoration of resiliency of any single object is also constrained by the destination drive or node.
  4. The restoration of multiple objects (such as Object A & D in the above example) is constrained by the same drive or node which will result in contention and slow the process of restoring resiliency to both objects.
  5. The impact of the recovery is High on virtual machines running on the source and destination nodes.
  6. The recovery of an Object is constrained by the source and destination node per object.
  7. Object stores generally require a witness, which is stored on another node in the cluster. (Not illustrated above)

It should be pointed out, where SSDs are used for a write cache, this can help reduce the impact and speed up recovery in some cases, but where data needs to be recovered from outside of cache, i.e.: A SAS or SATA drive, the fact writes go to SSD makes no difference as the writes are constrained by the read performance.

Summary:

Traditional RAID used by SAN/NAS and newer Hyper-converged Object based storage both suffer similar issue when recovering from drive or node failures which include:

  1. The restoration of resiliency is constrained by the source drive or node
  2. The restoration of resiliency is constrained by the destination drive or node
  3. The restoration is high impact on the desination
  4. The recovery of one object is constrained by the network connectivity between just two nodes.
  5. The impact of the recovery is High on any data (such as virtual machines) running on the RAID or source/destination node/s
  6. The recovery of RAID or an Object is constrained by a single part of the infrastructure being a RAID controller / drive or a single node.

In Part 2, we will look at the Hyper-converged Distributed File Systems.

Unlimited VMs per datastore? Its not a myth with Nutanix!

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For many years, I have been asked on countless occasions questions relating to how many VMs can (or should) be placed in one datastore.

In fact, just this morning I was asked this same question, and I decided to whip up a quick post.

I have previously posted an Example Architectural Decision relating to Datastore sizing for Block based storage. What this example was aimed to show was a how things like RPO/RTO and performance should be taken into consideration when choosing a datastore size.

The above example is not a hard and fast rule, but an example of one deployment which I was involved in.

There is a great article written on this topic by VCDX, Jason Boche (@jasonboche), titled  “VAAI and the Unlimited VMs per Datastore Urban Myth” which covers in great detail this topic as it relates to block based storage, being iSCSI, FC & FCoE.

But what about NFS, and what about with Hyper-converged solutions like Nutanix?

NFS has gained significant popularity in recent years, and in my opinion, people who know what they are talking about, no longer refer to NFS as “Tier 3 Storage” which was once common.

With traditional storage solutions, generally only a smaller number of controllers can actively serve IO to the one NFS mount, so the limiting factor preventing running more virtual machines per NFS mount, in my experience was performance but things like RPO/RTO were and are important considerations.

NFS does not suffer from SCSI reservations which resulted in increased latency ,which is what VAAI, specifically the Atomic Test & Set or ATS primitive helped too all but eliminate for block based datastores.

LUNs are limited by there queue depth, which in most cases is 32 (sometimes 64). This is also a limiting factor, as all the VMs in a datastore (LUN) share the same queue which can lead to contention. SIOC helps manage the contention by ensuring fairness based on share values, but it does not solve the issue.

NFS on the other hand has a much larger queue depth, in fact its basically unlimited as shown below.

NFSqueuedepth

So as NFS does not suffer from SCSI reservations, or queue depth issues, what is limiting us having hundreds or more VMs per datastore?

It comes down to how many active storage controllers are able to service the NFS mount, and the performance of the storage controller/s. In addition to this your business requirements around RPO/RTO. In other words, if a NFS mount is lost, how quickly can you recover.

For most traditional shared storage products,

1. Have only 1 or 2 active controllers – thus potentially limiting performance which would lead to lower VMs per NFS datastore.

2. Do snapshots at the NFS mount layer, so if you need to recover an entire NFS mount, the larger it is, the longer it may take.

For Nutanix, by default, NFS is used to present the Nutanix Distributed File System (NDFS) to vSphere, however the key difference between Nutanix and traditional shared storage is every controller in the Nutanix cluster, can and does Actively serve IO to any datastore in the cluster concurrently.

So the limit from a performance perspective is gone thanks to Nutanix scale out, shared nothing architecture, with one virtual storage controller (CVM) per Nutanix node. The number of nodes that’s can be scaled too, is also unlimited. An example of Nutanix ability to scale can be found here – Scaling to 1 million IOPS and beyond, Linearly!

Next what about the RPO/RTO issue? Well, Nutanix does not rely on LUNs or NFS mounts for our data protection (or snapshots), this is all done at a VM layer so your RPO/RTO is now per VM, which gives you much more flexibility.

With Nutanix, you can literally run hundreds or even thousands of VMs per NFS datastore, without performance or RPO/RTO problems thanks to scale out, shared nothing architecture and the Nutanix Distributed File System.

There are some reasons why you may choose to have multiple NFS datastores even in a Nutanix environment, these include, if you want to enable Compression and/or De-duplication which are enabled/disabled on a per container (or datastore) level. As some workloads don’t compress or dedupe well, these types of workloads should be excluded to reduce the overhead on the cluster.

It is important to note, Nutanix uses a concept called a “Storage Pool” which contains all the storage for the Nutanix cluster. On top of a “Storage Pool” you create “Containers” (or datastores). This means regardless of if you have 1 or 100 datastores, they all still sit on top of the one “Storage Pool” which means you still have access to the same amount of storage capacity, with no silos for maximum capacity utilization (and performance!).

Lastly, Nutanix does not suffer from the same availability concerns as traditional shared storage where a single LUN could potentially be lost. This is due to the distributed architecture of the Nutanix solution. For more information on how Nutanix is more highly available than traditional shared storage, check out “Scale out, Shared Nothing Architecture Resiliency by Nutanix”

Check out a screen shot of one cluster with ~800 VMs on a single datastore. Note: The sub millisecond latency and 14K IOPS w/ ~900MBps throughput. Not bad!

800VMsonDatastore

Scale Out Shared Nothing Architecture Resiliency by Nutanix

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At VMware vForum Sydney this week I presented “Taking vSphere to the next level with converged infrastructure”.

Firstly, I wanted to thank everyone who attended the session, it was a great turnout and during the Q&A there were a ton of great questions.

I got a lot of feedback at the session and when meeting people at vForum about how the Nutanix scale out shared nothing architecture tolerates failures.

I thought I would summarize this capability as I believe its quite impressive and should put everyone’s mind at ease when moving to this kind of architecture.

So lets take a look at a 5 node Nutanix cluster, and for this example, we have one running VM. The VM has all its data locally, represented by the “A” , “B” and “C” and this data is also distributed across the Nutanix cluster to provide data protection / resiliency etc.

Nutanix5NodeCluster

So, what happens when an ESXi host failure, which results in the Nutanix Controller VM (CVM) going offline and the storage which is locally connected to the Nutanix CVM being unavailable?

Firstly, VMware HA restarts the VM onto another ESXi host in the vSphere Cluster and it runs as normal, accessing data both locally where it is available (in this case, the “A” data is local) and remotely (if required) to get data “B” and “C”.

Nutanix5nodecluster1failed

Secondly, when data which is not local (in this example “B” and “C”) is accessed via other Nutanix CVMs in the cluster, it will be “localized” onto the host where the VM resides for faster future access.

It is importaint to note, if data which is not local is not accessed by the VM, it will remain remote, as there is no benefit in relocating it and this reduces the workload on the network and cluster.

The end result is the VM restarts the same as it would using traditional storage, then the Nutanix cluster “curator” detects if any data only has one copy, and replicates the required data throughout the cluster to ensure full resiliency.

The cluster will then look like a fully functioning 4 node cluster as show below.

5NodeCluster1FailedRebuild

The process of repairing the cluster from a failure is commonly incorrectly compared to a RAID pack rebuild. With a raid rebuild, a small number of disks, say 8, are under heavy load re striping data across a hot spare or a replacement drive. During this time the performance of everything on the RAID pack is significantly impacted.

With Nutanix, the data is distributed across the entire cluster, which even with a 5 node cluster will be at least 20 SATA drives, but with all data being written to SSD then sequentially offloaded to SATA.

The impact of this process is much less than a RAID rebuild as all Nutanix controllers in the cluster participate and take a portion of the workload as a result the impact per disk, per controller ,per node and importantly for production VMs running in the cluster, is greatly reduced.

Essentially, the larger the cluster, the faster the cluster can repair itself, and the lower the impact on production workloads.

Now lets talk about a subsequent ESXi host failure, now we have two failed nodes, and three surviving nodes, and only one copy of data “A” , “B” and “C” as shown below.

Nutanix5NodeCluster2failures1copydata

Now the Nutanix “Curator” detects only one copy of data “A”, “B” and “C” exists and starts to replicate copies of “A”, “B” and “C” across the cluster. This results in the below which is a fully functional and redundant cluster, capable of surviving yet another failure as shown below.

Nutanix5NodeCluster2Failures

Even in this scenario, where two ESXi hosts are lost, the environment still has 60% of its storage controllers (and performance), as compared to a typical traditional storage product where the loss of just two (2) controllers can have your environment completely offline, and even if you only lost a single controller, you would only have 50% of the storage controllers (and performance) available.

I think this really highlights what VMware and players like Google, Facebook & Twitter have been saying for a long time, scaling out not up, and shared nothing architecture is the way of the future. The only question is who will be dominant in bringing this technology to the mass market, and I think you know who I have my money on.