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    SNIA Storage Developer Conference-The Knowledge Continues

    October 13th, 2016

    SNIA’s 18th Storage Developer Conference is officially a success, with 124 general and breakout sessions;  Cloud Interoperability, Kinetiplugfest 5c Storage, and SMB3 plugfests; ten Birds-of-a-Feather Sessions, and amazing networking among 450+ attendees.  Sessions on NVMe over Fabrics won the title of most attended, but Persistent Memory, Object Storage, and Performance were right behind.  Many thanks to SDC 2016 Sponsors, who engaged attendees in exciting technology … Continue reading


    Storage Performance Benchmarking Q&A – Take 2

    December 16th, 2015

    Our recent Ethernet Storage Forum Webcast, “Storage Performance Benchmarking: Part 2,” has already been viewed by more than 500 people. If you haven’t seen it yet, it’s now available on –demand. Our expert presenters, Ken Cantrell and Mark Rogov did a great job fielding questions during the live event, but of course there wasn’t time to get to them all. So, as promised here are their answers to all of them. If you have additional questions or thoughts, please comment on this blog and we’ll get back to you as soon as we can.

    Q: “As an example, am I right to presume workloads are generated by VMs”

    A: Ken: It is probably a good idea at this point to define a workload, since we continue to use the term. At a very high level, think of a workload as the mix of operations issued by an application related to the accessing of data. In our case, data stored or made available by a storage solution. With that in mind, absolutely, workloads can be generated by VMs. But they don’t have to be. In other words, “it depends.”

    For example, consider these 3 cases:

    1) If your SUT (solution under test) was just a simple laptop with no hypervisor and a traditional OS, then there would be no VM in the mix. Your workload would be generated by the application you were measuring (whether that was a simple file copy or something complex like a local database installation).

    2) Your SUT is composed of a physical client (like the laptop above) attached to a machine with a hypervisor installed on it and a local guest OS installation that is capable of exporting NFS or SMB shares. The laptop sends I/O via Ethernet to the guest OS. In this example, there is a VM, but it is acting as the storage system, not the workload generator.

    3) Now reverse the I/O of example 2. Have the laptop export an SMB share and have the guest OS issue I/Os to that share. Now you finally have VMs generating workloads.

    A: Mark: If one examines the solution under test (SUT), and considers the general data flow, then the workload is generated by the clients/hosts layer. Yes, we indicated that the clients/hosts can be VMs, but they also could be physical systems, and, in the case of a SUT consisting of just one laptop, the workload is generated by the application.

    Q: Are you going to get around to file performance benchmarking? This infrastructure stuff is not new to me. I have done block all my life, I am interested in stuff about file.

    A: Ken: That’s the plan. We are still working out the exact sequence, timing and content for future presentations, but had a dedicated section on both block and file on the original roadmap. If you have specific topics within “file” that you’d like covered, respond in the comments. No promises to cover them, but knowing the desires of the audience is always a good thing.

    Keep in mind the intention of the webcast series – lay a strong, but simple foundation, for storage performance fundamentals and then build on that foundation.

    A: Mark: The main intent of the series is to lay down basic performance principles first, then build on them to go to more complex topics. Both Ken and I refer to ourselves as “File Heads” and we can’t wait to concentrate on just file, but it would only make sense given that the infrastructure foundations are firm and understood by our audience.

    Q: Why doesn’t SPEC SFS have performance testing for such failure models?

    A: Ken: Brighttalk provided this comment in isolation, so it isn’t entirely clear which failure models you’re asking for.  I’m assuming you mean a failure like a drive or controller failure. With that assumption, that said, the SFS subcommittee welcomes publications that illustrate a failure condition. SPEC SFS 2014 provides an excellent opportunity for someone to publish once in a non-failure scenario and then again in a failure condition of some sort – as long as that failure condition doesn’t violate any of the run rules regarding stable storage and the failure condition doesn’t generate user visible errors.

    Note that SPEC SFS 2014 doesn’t mandate any demonstration of failure scenarios. We’ve discussed this in the past, but it has never been a priority for those that participate in SPEC (which is open to all – see http://www.spec.org for instructions on how to join the SPEC Open Systems Group).

    Q: Why is write cache turned off for enterprise drives?

    A: Ken: I knew this question was coming. :) This is related to “stable storage” – the guarantee from your storage provider that data they say is safely stored on disk is actually stored on disk. I should clarify that the comment is a little dated and refers to caches designed around volatile memory; this wouldn’t apply to a hybrid SSD/spinning media drive that used the SSD to cache/stage data, since SSDs are non-volatile.

    Consider the failure scenario where the enterprise drive has write caching enabled and then experiences a power failure. In most every system sold, the storage controller treats drives pretty much as black boxes – they tell the drive to read or write data at a certain location and expect the drive to do as told. So, when the drive says “yup, I got that data, you’re good!” the storage solution trusts the drive and, when it doesn’t need it in memory any longer, throws it away (that data is safely stored on disk, so this is ok). If the drive chose to cache that information in volatile memory, and loses power, the information is gone.

    Midrange and enterprise storage vendors often (I think I can say generally) provide some sort of battery backup in case of power failures. These battery units keep power to at least some of the drives – but remember that drives (especially spinning ones) suck down a lot of power, and often the implementation chooses to keep power only to certain drives that the storage controller uses to flush its own volatile memory structures to.

    A quick Internet search shows some specific comments on this topic:

    From Seagate (http://knowledge.seagate.com/articles/en_US/FAQ/187751en):

    Windows 2000 Professional / Server, Windows XP Home / Professional, Windows Vista and Windows 7 have a nifty little feature called write caching buried within the depths of property tabs. Normally, this type of feature is used with SCSI drives in server applications to provide greater data integrity.

    When drives employ write-back cache, any interruption of power to the drive or system may cause lost or corrupted data because the drive does not have time to write the cached data to the disk before the power is lost. However, when write cache is turned off, drive performance slows down.

    From Microsoft (https://support.microsoft.com/en-us/kb/259716):

    …In addition, enabling disk write caching may increase operating system performance. This article describes how to enable or disable disk write caching…

    NOTE: Enabling write caching generates the following warning. This is normal:

    By enabling write caching, file system corruption and/or data loss could occur if the machine experiences a power, device or system failure and cannot be shutdown properly.

    Q: What’s the difference between CPU and ASIC? When to use which word?

    A: Ken: Unfortunately, the SNIA dictionary doesn’t define either term. At the easiest level, both are acronyms. CPU = central processing unit, and ASIC = application specific integrated circuit. At the next level, think of a CPU as general purpose processing element and an ASIC as a custom designed microchip designed for a special application or purpose. Once created, ASICs are non-programmable – they do something very specific (and hopefully very well and very quickly/efficiently). A CPU can run your bitcoin mining program overnight, wake you to Spotify in the morning, let you use your favorite word processor in between games of Plants vs. Zombies, and still let you watch Hulu before you head off to bed.

    An ASIP (Application Specific Instruction-Set Processor) bridges the gap between a general purpose processor (CPU) and the highly specific, targeted design of an ASIC. An ASIP will have a much reduced instruction set and a more targeted design towards a specific application (say, digital signal processing), but still allow the execution of a specific instruction set given to it.

    Q: Can you mention tools to identify the bottlenecks?

    A: Ken: We are trying very hard, particularly in the webcasts themselves, to stay vendor neutral. I don’t mind violating that though here in the Q&A a little bit.

    From an open source standpoint, there are a number of tools. One of the more popular now is to use something like Graphana as a front-end to Graphite, and use that to monitor a set of open source (or privately designed) sensors, including sensors from OPM below, that you place throughout your environment.

    Here are a few other open-source benchmarking and performance tools, and what aspect of performance to which they apply. Please note that this is not a comprehensive list, nor is this a recommendation for their use. We are providing the link as a convenience, not an endorsement. [http://www.opensourcetesting.org/performance.php]

    Still free, but NetApp-centric, is OnCommand Performance Manager (OPM), specifically OPM v2.0 and later. In addition to providing performance metrics for your NetApp storage array, OPM offers up the concept of a “bully” and “victim” scenario – it specifically watches for components that are performing poorly (the “victims”) and helps identify which other components are causing that poor behavior (the “bullies”). My team helps develop OPM.

    Not free, and not NetApp centric, but a NetApp product, is OnCommand Insight (OCI). This is a premier product for looking at the performance of the components across your datacenter.

    A: Mark: I didn’t want to break the vouch to neutrality. :) EMC has a number of tools as well, vRealize suite, ViPR SRM, Unisphere, plus platform specific tools… However, it has been my experience that the most important tool a performance expert has is still the critical mind. One observes the problem, and then walks the entire set of the SUT layers looking for incongruences. Too often, the perceived bottleneck is not the problem, but a manifestation of a problem somewhere else. For example, as we pointed in the “MiB/s section” of this webcast, the network layer was a bottleneck due to the badly configured OS multipath drivers. Deciphering cause from reason requires several things: a good understanding of the SUT and its layers; a critical mind to analyze problem conditions; and a large dose of curiosity. The latter one a personal trait that drives us to question “what if I change this to that?” Asking questions while troubleshooting is, IMHO, a cornerstone requirement and inherently, a very human trait. My personal view is that tools are just tools, and they require a human hand to operate and a human mind to analyze the results.

    Q: Did All flash arrays almost eliminate bottleneck..,at least the Storage controller bottleneck can be eliminated if enterprise can afford all flash arrays?

    A: Ken: Actually, almost exactly the opposite. Spinning drives are (now, at least) relatively slow. Over the past 10 years the drives have gotten much bigger, although HDD drive speeds haven’t really changed all that much,. Because of this, what I’ve observed is that the IOPS/GB ratio for HDD has, if anything, been getting worse* and the most common bottleneck for an HDD-based customer turns out to be the speed of their drives.

    Now consider what happens when a customer moves to SSDs. The SSDs that are sold (and folks can afford) are generally much smaller than the HDDs they are used to, so customers buy as many of them as they can in order to meet their capacity requirements. And the SSDs are, one-for-one, much faster. So what happens? High drive counts + really fast drives = the drives aren’t your bottleneck anymore. Instead, the bottleneck shifts upstream … in a well architected solution, generally to the storage controller or clients.

    *For those that know the terms, we could have a long discussion about working set sizes over the years, how fast data ages, tiered storage and such, and the effect that these have on observed iops/GB … but I think we could agree that since HDD speeds aren’t increasing, the iops/GB ratio isn’t generally getting better.

    Q: Can I download this slides?

    A: Ken: Absolutely. Here are the links to Part 1 of our series:

    PPT and PDF: http://www.snia.org/forums/esf/knowledge/webcasts (look for “Storage Performance Benchmarking: Introduction and Fundamentals (July 2015)”)

    Presentation Recording: https://www.brighttalk.com/webcast/663/164323

    Q&A Blog: http://sniaesfblog.org/?p=447

    Here are the links to Part 2:

    PPT and PDF: http://www.snia.org/forums/esf/knowledge/webcasts (look for “Storage Performance Benchmarking: Part 2 (October 2015)”)

    Presentation Recording: https://www.brighttalk.com/webcast/663/164335

    Q&A Blog: That’s what you’re reading now. :)

    Q: Storage controller, is a compute node, right? And for hyper converged systems, storage controller and compute nodes are the same, right?

    A: Mark: Most certainly, a Storage Controller can be a compute node, but in our webcast it is not. The term “compute node” is typically interpreted to be a part of the client/hosts layer. A compute node computes for the application, and such application is generating the workload (please see the question above about where workload is initiated).

    A good example of the compute node would be a system that renders cartoons, or geodesic fields. As such compute node computes something (application does the work), and stores the results onto the storage controller.

    However, in the case of hyper-converged infrastructure, the storage controller is often virtualized among the client hosts, making every compute node a part of a larger storage controller.

    Q: Are the performance numbers that vendors publish typically front-end?

    A: Mark: I don’t want to generalize published numbers as being one way or another. I recommend reading every publication for specific details. Vendors publish numbers to cover use cases, and each use case may come with its own set of expected measurement points and metrics. Ken and I talked about how metrics matter in the first Storage Performance Benchmarking webcast. :)

    Q: “We did an R&D PoC using 32 flash 400GB elements attached on DIMM slots (not through SAS controller, not a direct PCIe attach) and seven 40Gbps cards. We were able to pump 5.5M 4KB-IOPS resulting in 30GB/s (240Gbps) of traffic on the front-end connect. When do you expect the front-end connect be the bottleneck for more standard environment?”

    A: Ken: Woot! That sounds like a lot of fun. If you’re in the Raleigh, NC area and can talk about that not under NDA, we should have lunch. I’d like to hear more.

    I have a suspicion that this answer won’t satisfy you, because it isn’t going to be as empirical as your example. The problem in answering with raw numbers is that there isn’t a standard configuration for a SUT. An enterprise class storage array with a mix of 40GbE and 32GB FC connections (with traffic over both) will look very different than someone using their old Windows XP box with a single 100Mbit to share out an SMB share, and both will look different than someone accessing their photos on their favorite cloud provider. So, I’ll answer the question by saying that I expect the front-end connect to be the bottleneck anytime the rest of the components in the SUT are capable of hitting your performance metrics (whether that be in terms of response time, IOPS, or data rates), and the front-end connect isn’t.

    THAT said, you’d be astounded how often, even today, that MTU mismatches result in terrible front-end performance (and functionality).

    Q: “Example of cache in front end connection?”

    A: Ken: I’ll cheat and note that SUTs can be a lot more complicated than we showed. For example, our picture looked like this:

     

    Benchmarking Image 1

    Consider a SUT then where you have:

    Benchmarking Image 2

    At one level, “the internet” is just a big black box acting as our front-end connect. But if we zoom in on it, perhaps we find that somewhere along the line there’s a caching server. Then we have an easy answer to where you find cache in the front-end connect.

    In the much simpler model that you’ll find in many enterprise data environments though, you’ll find that the front-end connect consists of some relatively short length cables and a set of switches – either SAN or NAS switches. And in those environments, you won’t find a lot of cache. You will find memory, but you’ll find it used for buffering more than for caching.

    We tried to minimize this in the presentation since there’s not a universally agreed upon distinction between these two terms. I think of a buffer (in this context) primarily as memory set aside to hold data very briefly, after which it is consumed and removed from the buffer. I think of cache, on the other hand, as a storage medium that holds data specifically to speed up data access (storage or retrieval). Data held in a cache can be held for a very long time, and not all data in a cache may ever be consumed/used.

    Q: Even SSDs suck at random write but they are good for random read, is there that much difference?

    A: Ken: Yes. The data we pulled for drive speeds was real data. And keep in mind that “sucks” is pretty relative here. Enterprise SSDs still tend to be at least 4x faster than spinning media. And, very importantly, their performance is much more consistent and deterministic since seek time is irrelevant with an SSD. New NVRAM technologies, like 3D XPoint, promise to dramatically improve write performance.

    DRAM is volatile though so replacing HDDs with that wouldn’t really work, right? But if capacity requirements are high, we cannot replace disks with the cache, right?

    A: Mark: Cache should never replace capacity. Cache is temporary storage, and requires by design to move its data to a permanent storage location. The size of cache should be matched to the size of the data that application uses, so called “working area”. For example, if an application writes to a 4GB file (think VMware vmdk), then for best performance the entire 4GB should fit into cache. However, capacity requirements for a VMware datastore can be as high as several TB. If the application (ESX server) is running many VMs, perhaps only the performance few need to fit into cache, while all other VMs would use cache for sub-portions of their vmdks.

    A: Ken: You’re right, not permanently. As Mark points out, it isn’t to permanently replace the slower storage with cache necessarily, just supplement it enough that the working set fits in.

    Q: How can we make client do less IO? Will it make sense?

    A: Mark: A client does less IO by using larger IO size, for example. A classic use case is read- and write-sizes within NFS protocol. It is possible to increase read- and write-size of the NFS protocol from the NFS client mount options side. By default, some Linux environments use 32KB for reads and writes. And reading a 1GB file takes 32768 32KB IOs. If the read size is increased to 1GB, then it takes only 1024 IOs – a 32x reduction!

    A: Ken: Other options involve app re-writes (yes, sometimes these ARE possible) and OS upgrades. Perhaps in the “app-rewrite” category, or maybe a new category, I’ve also worked with developers to rewrite their DB queries to be much less disk intensive, for example.

    Q: Can you elaborate on some of the client level cache types other than file system or OS?

    A: Mark: Other than file system and OS? Hmm… Let’s see: PCI-based cards that cache block-device level cache, e.g. EMC VFCache, SanDisk Fusion-IO, NetApp Flash Cache. Native network protocols (CIFS and NFS) caches. Local database caching, e.g., SafePeak, TimesTen, Windows Azure Caching.

    Q: Please add more sessions, which goes into every detail

    A: Mark: Absolutely! We will! Promise! We’re shooting for Part 3 in Q1 2016.


    Storage Performance Benchmarking Q&A

    August 12th, 2015

    Our recent SNIA-ESF webcast, “Storage Performance Benchmarking: Introduction and Fundamentals” really struck a chord! It was our most highly rated and well attended webcast to date with more than 300 people at the live event. If you missed it, it’s now available on-demand. Thanks again to my colleagues, Ken Cantrell and Mark Rogov, who did an outstanding job explaining the basics and setting the stage for our next webcast in this series, “Storage Performance Benchmarking: Part 2“ on October 21st. Mark your calendar! Our audience had many great questions and there just wasn’t enough time to answer them all, so as promised, here are answers to all the questions we received.

    Q. Can you explain the difference between MiB and MB?

    A. The difference lies between the way that bytes are calculated. A megabyte (MB) is defined in decimal (base-10), and a mebibyte (MiB) in binary (base-2). There is a similar relationship between kilobytes (base-10) and kibibytes (KiB). So, if you begin with a single byte:

    1 kilobyte (KB) = 1000 bytes (B)

    1 kibibyte (KiB) = 1024 bytes (B)

    1 megabyte (MB) = 1000 KB = 1000 * 1000 bytes = 1,000,000 bytes

    1 mebibyte (MiB) = 1024 KiB = 1024 * 1024 bytes = 1,048,576 bytes

    The distinction is important because very few applications or vendors make use of the MiB notation and instead use the MB notation to refer to binary numbers. If vendor or tool A is using MB to refer to base-10 measurements, and vendor or tool B is using MB to refer to base-2 measurements, it may falsely appear that the two vendors or tools are performing differently (or the same), when in fact the difference (or similarity) is simply because they are labeling two different things (MB and MiB) as the same (MB).

    Telecommunications and networking generally measure and report in MB. Disk drive manufacturers tend label storage in terms of MB. Many operating systems report storage labeled as MB, but calculated as MiB. Storage performance (whether from an application’s view, or the storage vendor’s own tools) is generally measured in binary (MiB/s), but reported in decimal (MB/s).

    Q. I disagree with the conflation of the terms “IOPS” and “throughput.” Throughput generally refers to overall concept of aggregate performance. IOPS measure throughput at a given request size, which most clients assume to be small blocks (~512B-4K). Many clients would say that bandwidth, measured in MB/s or GB/s, is a measure of throughput @ large block size (>~128K). Performance typically varies significantly @ different block sizes. So clients need to understand that all 3 of these concepts differ.

    A. The SNIA dictionary equates throughput to IOPS, which is why we listed it as a common alternative for IOPS.  However, the problem with “throughput” is in the industry’s lack of agreement on what this term means. See the examples below, where sometimes it clearly refers to IOPS and other times to MB/s.  This is why, in the end, we recommend you simply don’t use the term at all, and use either IOPS or MB/s.

    Throughput = MB/s

    Techterms.com: “Throughput refers to how much data can be transferred from one location to another in a given amount of time. It is used to measure the performance of hard drives and RAM, as well as Internet and network connections.”

    Meriam-Webster: “The amount of material, data, etc., that enters and goes through something (such as a machine or system)”

    Throughput = IOPS

    SNIA Dictionary: “Throughput:  [Computer System] the number of I/O requests satisfied per unit time.   Expressed in I/O requests/second, where a request is an application request to a storage subsystem to perform a read or write operation.”

    Wikipedia:  “When used in the context of communication networks, such as Ethernet or packet radio, throughput or network throughput is the rate of successful message delivery over a communication channel. The data these messages belong to may be delivered over a physical or logical link, or it can pass through a certain network node. Throughput is usually measured in bits per second (bit/s or bps), and sometimes in data packets per second (p/s or pps) or data packets per time slot.”

    TechTarget:  “Throughput is a term used in information technology that indicates how many units of information can be processed in a set amount of time.”

    Throughput = Either

    Dictionary.com:  “1. The rate at which a processor can work expressed in instructions per second or jobs per hour or some other unit of performance. 2. Data transfer rate.”

    Q. So, did I hear it right? IOPS and Throughput are the same and thus can be used interchangeably?

    A. Please see definitions above.

    Q. The difference between FE/BE is the raid level + Write percentage = overhead IOPS. It is different at times due to RAID or any virtualization at the storage level, like if you have RAID 5, every front-end write would have 4 backend write. What happens if the throughput at the back end of a controller is more than at the front end?

    A. The difference between Front End (FE) and Back End (BE) is generally the vantage point. One of the objectives of the presentation was to show that when comparing two results (or systems) the IOPS must be measured at the same point. BE IOPS depend, in part, on the protection overhead and on the software engine’s ability to counteract it. But instead of defining why there is a difference, we aimed at exposing that such difference exists. The “whys” will be addressed in the future webcasts.

    Q. Also, another point that would dictate which system is the storage capacity each has to offer. Very good compare & contrast illustration.

    A. Agreed, although capacity itself has several facets: raw capacity or the sum of manufacture advertised capacities of all internal drives; usable capacity or the amount of data that could be written on one system after all formatting and partitioning; and logical capacity or the amount of data that external hosts could write onto the system. Logical may be different from usable based on some data reduction services that a system could offer.

    Q. Why is there a lower limit on your acceptable latency band? Storage can be too fast for your business needs?

    A. We had some discussions looking at whether you show it as a cap, or a band. In many cases, customers will have a target latency cap, but allow some level of exceptions (either % of time it is exceeded, or % by which it is exceeded). That initially suggested a soft cap and a hard cap. One of us also has seen customers that highly value consistent behavior. In a service provider context, providing “too fast” performance could actually build a downstream customer expectation that will cause satisfaction issues if performance later degrades, even if it degrades to levels within the original targets.  You could certainly see this more generally as a cap though, which Ken stated verbally in the presentation. The band represents not only the cap, but also variance.

    Q. What is an “OP”? An”Operation Per”? Shouldn’t this be “$/OPS” or “dollars per operation per second”?

    A. This question was raised during the verbal Q&A. An “OP” in our context was any of the potential protocol-level operations. For example, in an NFSv3 context, this could be a CREATE, SETATTR, GETATTR, or FINDFILE operation. The number of these that occur in a second is what we referred to as OPS. There is certainly potential confusion here, and we struggled for consistency as we put the presentation together.

    For example, sometimes we (and others) refer to operations per second as “op/s” instead of OPS, or use “Ops” or “OPs” or “ops” to mean “more than one operation” instead of “operations per second.” We chose to use OPS because it is consistent with IOPS, which we see frequently in this space, and are trying to push others towards this terminology for consistency.

    Q. More IOPS in the same amount of time seems to imply that the system with more IOPS is performing each IO in less time than the other system. So how do we explain a system that can do more IOPS than another system but with a higher response time for each IO than the other system?

    A. Excellent question! Two different angles on a reply follow …

    First angle:

    A simple IOP count doesn’t include the size of the IOP, or the work that the storage controller must perform to process it. Therefore comparing just the IOPS does not provide a good comparison: what if system 1 was doing large IOs (64KiB or more) and system 2 was doing small IOs (4KiB or less)? To avoid that, most people fix the IO size or run controlled tests changing the IO size in steps and then compare the arrays.

    The point that we are making with the webcast is that IOPS alone are not enough for a comparison, one must look beyond that into IO size, response time and above all business requirements.

    Second angle:

    This is an excellent question, and there are at least two answers.

    To begin, we need to explain the relationship between IOPS and response time.

    We generally talk about response time (latency) in terms of seconds or milliseconds. We say things like “a response time of 5ms.” But this is actually a shortcut. It is really “seconds per I/O” or “milliseconds per I/O.” Also, remember that in “IOPS” the “p” stands for “per”, so this is “IOs per second.” So, ignoring the conversions between “seconds” and “milliseconds”, the relationship between IOPS and response time is easy…they are inverses of each other:

    blog image 1

    So:

    blog image 2

    Implies:

    blog image 3

    Example:

    blog image 4

     

    blog image 5

    Given this:

    The first answer to your question is tied to idle time. Consider the following simple example.

    System 1:

    For system1, assume that the storage array service time is the only significant contributor to latency.

    Client issues an I/O

    Storage array services the I/O in 10ms

    As soon as the client sees the response, it immediately issues a new I/O

    Process continues, with all I/Os serviced by the storage array in 10ms each

    How many IOPS is system 1 doing?  Measured at the client, or the storage array, it is doing 100 IOPS with an average response time of 10ms.  Remember the relationship between IOPS and response time from the intro above.

    System 2:

    For system2, assume that both the client and storage array contribute to latency.

    Client issues an I/O

    Storage array services the I/O in 5ms.

    Client waits 5ms before issuing a new I/O.

    Process continues, with all I/Os being serviced by the storage array in 5ms each, and the client waiting 5ms between issuing each I/O.

    How many IOPS is system2 doing? If you measure the total number of I/Os completed in a minute, it is completing the same # as System1 and so doing 100 IOPS. But what is the latency? If you measured the latency at the storage array, it would be 5ms. If you measured at the client, it would depend on whether your measurement was above or below the point where the 5ms delay per I/O was inserted, and would see either 5ms or 10ms.

    The second answer is tied to “concurrency,” an element we didn’t discuss in the webcast.

    Concurrency might also be called “parallelism.”  In the examples above, the concurrency was ‘1’. There was only ‘1’ I/O active at a time.

    What if we had more though?  Example:

    For both systems, assume that the storage array service time is the only significant contributor to latency.

    System 1 (same as above)

    Client issues an I/O

    Storage array services the I/O in 10ms

    As soon as the client sees the response, it immediately issues a new I/O

    Process continues, with all I/Os serviced by the storage array in 10ms each

    How many IOPS is system 1 doing?  Measured at the client, or the storage array, it is doing 100 IOPS with an average response time of 10ms. Remember the relationship between IOPS and response time from the intro above.

    System 2:  Increased concurrency

    Client issues two I/Os

    Storage array receives both and is able to handle both in parallel, but they take 20ms to complete

    As soon as the client sees the response, it immediately issues two new I/Os

    Process continues, with all I/Os serviced by the storage array in 20ms each, and the client always issuing two at a time

    How many IOPS is system 2 doing? In the aggregate, measured at the client or the storage array, it is doing 100 IOPS. What is the response time? This is where the relationship between IOPS and response time introduced before can break down, depending on what you’re really asking for and it really takes some queuing theory to properly explain this. Ignoring the different kinds of queuing models and simplifying for this discussion, at a high level you could say that the average response time is still 10ms. Some reporting tools are likely to do this. That is misleading however, since each I/O really took 20ms. You’d only see a 20ms response if (a) you actually kept track of the real response times somewhere [which many systems do … they’ll create many different response time buckets and store counts of the # of I/Os with a given response time in a given bucket], or (b) you make use of Little’s Law. Little’s Law would at least give you a better mean response time. We didn’t discuss Little’s Law in the webcast, and I won’t go into much detail of it here (follow the Wikipedia link if you want to know more), but in short, Little’s Law can be rearranged to state that:

    blog image 6

    or, in our case above:

    blog image 7

    Q. What is valuable from SPEC’s SFS benchmark in this context?

    A. Excellent question. We think very highly of SPEC SFS© 2014 for a variety of reasons, but in this context, a few stick out in particular:

    1) The workloads provided by SPEC SFS 2014 are the result of multi-vendor research, actual workload traces, and efforts to target real business use cases. The VDI (virtual desktop infrastructure), SWBUILD (software build), DB (OLTP database) and VDA (video data acquisition) workloads are all distinct workloads with very different I/O patterns, I/O size mixes, and operation mixes. We’ll talk more in another session about these aspects of a workload, but the important element is that each workload is designed to help users understand performance in a specific business context.

    2) To encourage folks to think in a business context, SPEC SFS 2014 primarily reports results not in IOPS or MB/S, but in the relevant “business units” at a given response time, along with a measure of overall response time, aptly referred to as ORT (Overall Response Time).  For example, the SWBUILD workload reports load points as “number of concurrent software builds.” MB/s information is still available for those that want to dig in, but what is really important is not the MB/s, but how many software builds, or databases, or virtual desktops, or video streams can a system support, and this is where the reporting focus is.

    3) SPEC SFS 2014 recognizes that consistent performance is generally an important element of good performance. SPEC SFS 2014 implements a variety of checks during execution to make sure that each element doing I/O is doing roughly the same amount of I/O and that they are doing the amount of work that was requested of them and aren’t lagging behind.

    More information on SPEC SFS 2014 (and SPEC in general) is available from http://www.spec.org

    Q. Anything different/special about measuring and testing object storage?

    A. Object Storage is a hot topic in the industry right now, but unfortunately fell outside the scope of this fundamentals presentation. We will be examining object storage – and corresponding network protocols – in a later webcast. Stay tuned!

    Q. Does Queue depth not matter in response times?

    A. We did not define Queue Depth as a term in this webcast because we classified it as an advanced subject. We are planning to address queue depth during a future webcast. In short, the queue depth does play an important role in the performance world, but response time is affected by it only indirectly. When the target Queue is full, the Initiator can’t send any more IOs and waits for the Queue to free up.

    The time spent waiting is not response time. Response time measures the time the Target spends processing the IO it was able to receive (place into its queue). However, some scenarios may keep an IO inside a Queue for a long time (due to storage controller doing something else), and thus increase the response time for the waiting IO. In the latter case, increasing the depth of the Queue will increase the response time.

    Keep in mind thought that “It Depends”. It depends on where you measure. For example, a client may have no visibility into what is happening at the storage array. It cares about how long the storage array takes to respond … it doesn’t know or care about the differences between the storage array’s view of its service time (how long the storage array is doing work) and its queuing delay / wait time (how long it is waiting to do work).

    As far as the client is concerned, this is all response time. It just wants a response. Most “response time” metrics reported by tools / apps/ storage arrays really decompose into a set of true service times and queuing delays, although you may never get to see how those components come together w/o using other tools.

    Q. Order is important, in addition to mix ratios. For IOs, sequential vs. random (and specifics on randomness) is important. For file operations, where the protocol is stateful, the sequence of operations can strongly impact performance. Both block and file storage can have caches, which behave very differently depending on ordering and working set size. I suggest including the concept of order in a storage benchmarking intro.

    A. These are excellent points, but given the amount of time we had, we had to make some hard choices about what to include and what to drop (or defer). This fell on the chopping block. Someone once told me that the best talks are those where you cry over what you’ve been forced to leave on the cutting room floor. There was a lot of good stuff we couldn’t include. I expect that we will address this idea in the planned discussion of workloads.

    Q. The “S” in “OPS” and “IOPS” means Seconds and isn’t a pluralization. Some people make a mistake here and use the term “OP” and “IOP” to mean “1 operation/second” or “1 IO/second”. This mistake happens in this presentation, where “$/OP” is used. The numbers suggest what was intended is “# of dollars to get 1 operations/second.” The term “$/OP” can be misinterpreted to mean “dollars per operation,” which is a different but also relevant metric for perishable storage, like flash.

    A. Good catch. We tried hard to be consistent in using “OPS” instead of “op/s” or some variant, when we meant “operations per second”.  There is another Q&A that addresses this. Technically, we slipped up in saying “$/OP” instead of “$/OPS” when we meant, as you noted, “Dollars per (operations per second)”. That said, I think you’ll find that nearly everyone makes this shortcut/mistake. That doesn’t make it right, but does mean you should watch out for it.

    Q. Isn’t capacity reported in MiB, Gib etc and bandwidth in MB/s today?

    A. Unfortunately, our impression is that capacity is also reported in decimal, at least in marketing literature. All we were trying to point out is that units of measurements must be consistent at all points. Many people mix decimal and binary numbers creating ambiguities and errors (remember that at a TiB vs. TB level, the difference is 10%!) For example, graphs that show IO size in decimal and bandwidth in binary.

    Q. Would you also please talk about different SAN protocols and how they differ/impact the performance?

    A. There are additional webcasts under development that discuss the storage networking protocols, their performance trade/offs, and how performance can be affected by their use. Due to time constraints we needed to postpone this portion because it deserves more attention than we could provide in this session.

    Q. So even though IOPS could be more, the response time could be less…so it’s important to consider both. Is that correct?

    A. Absolutely! But just response time is not enough either! You need more load points, and a business objective to truly assess the value of a solution.