The recipe for storage performance modeling

Good morning, afternoon, evening, Ladies & Gentlemen, wherever you are.

Today, we are going to learn how to bake, errr … I mean, make a storage performance model. Before we begin, allow me to set the stage.

Don’t you just hate it when you are asked to do storage performance sizing and you don’t have a freaking idea how to get started? A typical techie would probably say, “Aiya, just use the capacity lah!”, and usually, they will proceed to size the storage according to capacity. In fact, sizing by capacity is the worst way to do storage performance modeling.

Bear in mind that storage is not a black box, although some people wished it was. It is not black magic when it comes to performance sizing because things can be applied in a very scientific and logical manner.

SNIA (Storage Networking Industry Association) has made a storage performance modeling methodology (that’s quite a mouthful), and basically simplified it into these few key ingredients. This recipe is for storage performance modeling in general and I am advising you guys out there to engage your storage vendors professional services. They will know their storage solutions best.

And I am going to say to you – Don’t be cheap and not engage professional services – to get to the experts out there. I was having a chat with an consultant just now at McDonald’s. I have known this friend of mine for about 6-7 years now and his name is Sugen Sumoo, the Director of DBORA Consulting. They specialize in Oracle and database performance tuning and performance forecasting and it is something that a typical DBA can’t do, because DBORA Consulting is the Professional Service that brings expertise and value to Oracle customers. Likewise, you have to engage your respective storage professional services as well.

In a cook book or a cooking show, you are presented with the ingredients used and in this recipe for storage performance modeling, the ingredients (in no particular order) are:

  • Application block size
  • Read and Write ratio
  • Application access patterns
  • Working set size
  • IOPS or throughput
  • Demand intensity

Application Block Size

First of all, the storage is there to serve applications. We always have to look from the applications’ point of view, not storage’s point of view.  Different applications have different block size. Databases typically range from 8K-64K and backup applications usually deal with larger block sizes. Video applications can have 256K block sizes or higher. It all depends.

The best way is to find out from the DBA, email administrator or application developers. The unfortunate thing is most so-called technical people or administrators in Malaysia doesn’t have a clue about the applications they manage. So, my advice to you storage professionals, do your research on the application and take the default value. These clueless fellas are likely to take the default.

Read and Write ratio

Applications behave differently at different times of the day, and at different times of the month (no, it’s not PMS). At the end of the financial year or calendar, there are some tasks that these applications do as well. But in a typical day, there are different weightage or percentage of read operations versus write operations.

Most OLTP (online transaction processing)-based applications tend to be read heavy and write light, but we need to find out the ratio. Typically, it can be a 2:1 ratio or 60%:40%, but it is best to speak to the application administrators about the ratio. DSS (Decision Support Systems) and data warehousing applications could have much higher reads than writes while a seismic-analysis applications can have multiple writes during the analysis periods. It all depends.

To counter the “clueless” administrators, ask lots of questions. Find out the workflow of several key tasks and ask what that particular tasks do at different checkpoints of the application’s processing. If you are lazy (please don’t be lazy, because it degrades your value as a storage professional), use a rule of thumb.

Application access patterns

Applications behave differently in general. They can be sequential, like backup or video streaming. They can be random like emails, databases at certain times of the day, and so on. All these behavioral patterns affect how we design and size the disks in the storage.

Some RAID levels tend to work well with sequential access and others, with random access. It is not difficult to find out about the applications’ pattern and if you read more about the different RAID-levels in storage, you can easily identify the type of RAID levels suitable for each type of behavioral patterns.

Working set size

This variable is a bit more difficult to determine. This means that a chunk of the application has to be loaded into a working area, usually memory and cache memory, to be used and abused by the application users.

Unless someone is well versed with the applications, one would not be able to determine how much of the applications would be placed in memory and in cache memory. Typically, this can only be determined after the application has been running for some time.

The flexibility of having SSDs, especially the DRAM-type of SSDs, are very useful to ensure that there is sufficient “working space” for these applications.

IOPS or Throughput

According to SNIA model, for I/O less than 64K, IOPS should be used as a yardstick to do storage performance modeling. Anything larger, use throughput, in which MB/sec is the measurement unit.

The application guy would be able to tell you what kind of IOPS their application is expecting or what kind of throughput they want. Again, ask a lot of questions, because this will help you determine the type of disks and the kind of performance you give to the application guys.

If the application guy is clueless again, ask someone more senior or ask the vendor. If the vendor engineers cannot give you an answer, then they should not be working for the vendor.

Demand intensity

This part is usually overlooked when it comes to performance sizing. Demand intensity refers to how intense is the I/O requests. It could come from 1 channel or 1 part of the applications, or it could come from several parts of the applications in parallel. It is as if the storage is being ‘bombarded’ by applications and this is the part that is hard to determine as well.

In some applications, the degree of intensity or parallelism can be tuned and to find out, ask the application administrator or developer. If not, ask the vendor. Also do a lot of research on the application’s architecture.

And one last thing. What I have learned is to add buffers to the storage performance model. Typically I would add about 10-20% extra but you never know. As storage professionals, I would strongly encourage to engage professional services, because it is worthwhile, especially in the early stages of the sizing. It is usually a more expensive affair to size it after the applications have been installed and running.

“Failure to plan is planning to fail”.  The recipe isn’t that difficult. Go figure it out.

3TB Seagate – a performance sloth

I can’t get home. I am stuck here at the coffee shop waiting out the traffic jam after the heavily downpour an hour ago.

It has been an interesting week for me, which began last week when we were testing the new Seagate 3TB Constellation ES.2 hard disk drives. It doesn’t matter if it was SAS or SATA because the disks were 7,200 RPM, and basically built the same. SAS or SATA is merely the conduit to the disks and we were out there maneuvering the issue at hand.

Here’s an account of  testing done by my team. My team has been testing the drives meticulously, using every trick in the book to milk performance from the Seagate drives. In the end, it wasn’t the performance we got but more like duds from Seagate where these type of drives are concerned.

How did the tests go?

We were using a Unix operating system to test the sequential writes on different partitions of the disks, each with a sizable GB per partition. In one test, we used 100GB per partition. With each partition, we were testing the outer cylinders to the inner cylinders, and as the storage gurus will tell you, the outer rings runs at a faster speed than the inner rings.

We thought it could be the file system we were using, so we switched the sequential writes to raw disks. We tweaked the OS parameters and tried various combinations of block sizes and so on. And what discovered was a big surprise.

The throughput we got from the sequential writes were horrible, started out with MB/sec lower almost 25% lower than a 2TB Western Digital RE4 disk, and as it went on, the throughput in the inner rings went down to single digit MB/sec. According to reliable sources, the slowest published figures by Seagate were in the high 60′s for MB/sec but what we got were close to 20+MB/sec. The Western Digital RE4 was giving out consistent throughput numbers throughout the test. We couldn’t believe it!

We scoured the forums looking for similar issues, but we did not find much about this.This could be a firmware bug. We are in the midst of opening an escalation channel to Seagate to seek explanation. I would like to share what we have discovered and the issue can be easily reproduced. For customers who have purchased storage arrays with 2 or 3TB Seagate Constellation ES/2 drives, please take note. We were disappointed with the disks but thanks to my team for their diligent approach that resulted in this discovery.

NFS deserves more credit from guys doing virtualization

I was at the RedHat Forum last week when I chanced upon a conversation between an attendee and one of the ECS engineers. The conversation went like this

Attendee: Is the RHEV running on SAN or NAS?

ECS Engineer: Oh, for this demo, it is running NFS but in production, you should run iSCSI or Fibre Channel. NFS is only for labs only, not good for production.

Attendee: I see … (and he went off)

I was standing next to them munching my mini-pizza and in my mind, “Oh, come on, NFS is better than that!”

NAS has always played a smaller brother to SAN but usually for the wrong reasons. Perhaps it is the perception that NAS is low-end and not good enough for high-end production systems. However, this is very wrong because NAS has been growing at a faster rate than Fibre Channel, and at the same time Fibre Channel growth has been tapering and possibly on the wane. And I have always said that NAS is a better suited protocol when it comes to unstructured data and files because the NAS protocol is the new storage networking currency of Internet storage and the Cloud (this could change very soon with the REST protocol, but that’s another story). Where else can you find a protocol where sharing is key. iSCSI, even though it has been growing at a faster pace in production storage, cannot be shared easily because it is block-based.

Now back to NFS. NFS version 3 has been around for more than 15 years and has taken its share of bad raps. I agree that this protocol is still very much in the landscape of most NFS installations. But NFS version 4 is changing all that taking on the better parts of the CIFS protocol, notably the equivalent of opportunistic locking or oplocks. In addition to that it has greatly enhanced its security, incorporating Kerberos-type of authentication. As for performance, NFS v4 added in a compounded in a COMPOUND operations for aggregating operations into a single request.

Today, most virtualization solutions from VMware and RedHat works with NFS natively. Note that the Windows CIFS protocol is not supported, only NFS.

This blog entry is not stating that NFS is better than iSCSI or FC but to give NFS credit where credit is due. NFS is not inferior to these block-based protocols. In fact, there are situations where NFS is better, like for instance, expanding the NFS-based datastore on the fly in a VMware implementation. I will use several performance related examples since performance is often used as a yardstick when these protocols are compared.

In an experiment conducted by VMware based on a version 4.0, with all things being equal, below is a series of graphs that compares these 3 protocols (NFS, iSCSI and FC). Note the comparison between NFS and iSCSI rather than FC because NFS and iSCSI run on Gigabit Ethernet, whereas FC is on a different networking platform (hey, if you got the money, go ahead and buy FC!)

Based a one virtual machine (VM), the Read throughput statistics (higher is better) are:

 

The red circle shows that NFS is up there with iSCSI in terms of read throughput from 4K blocks to 512K blocks. As for write throughput for 1 VM, the graph is shown below:


Even though NFS suffers in write throughput in the smaller blocks less than 16KB, NFS performance write throughput improves over iSCSI when between 16K and 32K range and is equal when it is in 64K, 128K and 512K block tests.

The 2 graphs above are of a single VM. But in most real production environment, a single ESX host will run multiple VMs and here is the throughput graph for multiple VMs.

Again, you can see that in a multiple VMs environment, NFS and iSCSI are equal in throughput, dispelling the notion that NFS is not as good in performance as iSCSI.

Oh, you might say that this is just VMs without any OSes or any applications running in these VMs. Next, I want to share with you another performance testing conducted by VMware for an Microsoft Exchange environment.

The next statistics are produced from an Exchange Load Generator (popularly known as LoadGen) to simulate the load of 16,000 Exchange users running in 8 VMs. With all things being equal again, you will be surprised after you see these graphs.

The graph above shows the average send mail latency of the 3 protocols (lower is better). On the average, NFS has lower latency than iSCSI, better than what most people might think. Another graph shows the 95th percentile of send mail latency below:

 

Again, you can see that the NFS’s latency is lower than iSCSI. Interesting isn’t it?

What about IOPS then? In another test with an 8-hour DoubleHeavy LoadGen simulator, the IOPS graphs for all 3 protocols are shown below:

In the graph above (higher is better), NFS performed reasonably well compared to the other 2 block-based protocols, and even outperforming iSCSI in this 8-hour load testing. Surprising huh?

As I have shown, NFS is not inferior compared to the block-based protocols such as iSCSI. In fact, VMware in version 4.1 has improved all 3 storage protocols significantly as mentioned in the VMware paper. The following are quoted in the paper for NFS and iSCSI.

  1. Using storage microbenchmarks, we observe that vSphere 4.1 NFS shows improvements in the range of 12–40% for Reads,and improvements in the range of 32–124% for Writes, over 10GbE.
  2. Using storage microbenchmarks, we observe that vSphere 4.1 Software iSCSI shows improvements in the range of 6–23% for Reads, and improvements in the range of 8–19% for Writes, over 10GbE

The performance improvement for NFS is significant when the network infrastructure was 10GbE. The percentage jump between 32-124%! That’s a whopping figure compared to iSCSI which ranged from 8-19%. Since both protocols are neck-to-neck in version 4.0, NFS seems to be taking a bigger lead in version 4.1. With the release of VMware version 5.0 a few weeks ago, we shall know the performance of both NFS and iSCSI soon.

To be fair, NFS does take a higher CPU performance hit compared to iSCSI as the graph below shows:

Also note that the load testing are based on NFS version 3. If version 4 was used, I am sure the performance statistics above will take a whole new plateau.

Therefore, NFS isn’t inferior at all compared to iSCSI, even in a 10GbE environment. We just got to know the facts instead of brushing off NFS.

What kind of IOPS and throughput do you get from RAID-5/6? – Part 2

In my previous blog entry, I mentioned the write penalty for RAID-5/6. This factor will figure heavily in the way we size the RAID-level for performance capacity planning.

It is difficult to ascertain what kind of IOPS and throughput that are required for an application, especially a database, to run well with additional room to grow. From a DBA or an application developer, I believe they would have adequate information to tell what is the numbers of users that the application can support, both average and peak, transactions per second (TPS), block size required for logs, database files and so on.

But as we are all aware, most of the time, these types of information are not readily available. So, coming from a storage angle, the storage administrator can advise the DBA or the application developer that the configured RAID group or volume or LUN is capable of delivering a certain number of IOPS and is able to achieve a certain throughput MB/sec. These numbers will be off the box itself immediately. Of course, other factors such as HBA speed, the FC/iSCSI configurations, the network traffic and so on will affect the overall performance delivery to the application. But we can safely inform the DBA and/or the application developer that this is what the storage is delivering out of the box.

The building blocks of all storage RAID groups/volumes/LUNs are pretty much your hard disk drives (HDDs) and/or Solid State Drives (SSDs). The manufacturer of these disks will usually publish the IOPS and throughput of individual drives but if these information is not available, we can construct IOPS of an individual HDD from its seek and latency times.

For example, if the HDD’s

average latency = 2.8 ms;          average read seek = 4.2 ms;              average write seek = 4.8 ms

then the IOPS can be calculated as

                                  1
         IOPS = ---------------------------------------
                (average latency) + (average seek time)

Therefore from the details above,

                    1
         IOPS = -------------------  = 136.986 IOPS
                (0.0028) + (0.0045)

That’s pretty simple, right? But of course, it is easier to just accept that a certain type of disk will have a range of IOPS as shown in the table below:

Disk Type RPM IOPS Range
SATA 5,400 50-75
SATA 7,200 75-100
SAS/FC 10,000 100-125
SAS/FC 15,000 175-200
SSD N/A 5,000-10,000

The information from the table above is just for reference only and by no means a very accurate one but it is good enough for us to determine the IOPS of a RAID group/volume/LUN. Let’s look at the RAID write penalty again in the table below:

RAID-level Number of I/O Reads
Number of I/O for Writes
RAID Write Penalty
0 1 1 1
1 (1+0, 0+1) 1 2 2
5 1 4 4
6 1 6 6

Next, we need to know what is the ratio of Reads vs Writes for that particular database or application. I mentioned earlier that in OLTP-type of applications, we usually take a 2:1 or 3:1 ratio in favour of Reads.

To make things simpler, let’s assume we create a RAID-6 volume of 6 data disks and 2 parity disks in a RAID-6 (6+2) configuration. The disks used are SATA disks of 7,200 RPM, with each individual disk of 100 IOPS. Assume we are using a ratio of 2:1 in favour of Reads, which gives us 66.666% and 33.333% respectively for Reads and Writes.

Therefore, the combined IOPS of the 8 disks in the RAID-6 configuration is probably about 800 IOPS. However, because of the write penalty of RAID-6, the effective IOPS for the RAID-6 volume will be lower than that. Let’s do some calculation to see what happens:

1)  Read IOPS + Write IOPS = 800 IOPS

2)  (0.66666 x 800) + (0.33333 x 800) = 800 IOPS

3) Read IOPS will be 0.66666 x 800 = 533.328 IOPS

4) Write IOPS will be 0.33333 x 800 = 266.664 IOPS. However, since RAID-6 has a write penalty of 6, this number has to be divided by 6. 266.664/6 will be 44.444 IOPS for Writes

Therefore, what the RAID-6 volume is capable of is approximately 533 IOPS for Reads and 44 IOPS for Writes.

We have determined IOPS for the RAID volume but what about throughput. Throughput is determined by the block size used. Assume that our RAID-6 volume uses a 4-K block size. With a combined effective IOPS of 577 (533+44), we multiply the IOPS with the block size

     Throughput = 577 IOPS x 4-KB
                = 2308KB/sec

Therefore when I/O is sustained in a sequential manner, the effective throughput is 2308KB/sec.

On the other hand, we often were told to add more spindles to the volume to increase the IOPS. This is true, to a point, where the maximum amount of IOPS that can be delivered will taper into a flatline, because the I/O channel to the RAID volume  has been saturated. Therefore, it is best to know that adding more spindles does not always equate to a higher IOPS.

Performance sizing for a database or an application is both a science and an art. Mathematically, we can prove things to a a certain amount of accuracy and confidence but each storage platform is very different in the way they handle RAID. Newer storage platforms have proprietary RAID that nowadays, it does not matter much what kind of RAID is best for the application. Vendors such as IBM XIV has RAID-X which both radical in design and implementation. NetApp will almost always say RAID-DP is the best no matter what, because RAID-DP is all NetApp.

So there is no right or wrong to choose the RAID-level for the application. But it is VERY important to know what are the best practice are and my advice is everyone is to do Proof-of-Concepts, and TEST, TEST, TEST! And ASK QUESTIONS!