Category Archives: EMC VMAX

EMC VMAX3 is a Data Services Platform

VMAX3 is now Generally Available and represents the next generation of market leading platforms from EMC that have established themselves as the most reliable and highest performing data storage platforms on the planet. Let’s examine what makes VMAX3 the first Data Services Platform in the industry and why VMAX3 is an even more revolutionary step from VMAX, as VMAX was from DMX. Along the way let’s dispel some myths about the product.

At launch VMAX3 is ready for the most mission critical workloads.  At its introduction, the original VMAX was a revolutionary platform, virtualizing the matrix interconnects between internal components and allowing for both scale-up and a scale-out expansion based entirely on Virtual Provisioning.  These were enormous leaps forward in the simplicity and scalability of Symmetrix.  EMC has developed rigorous testing, manufacturing processes, and incredible architecture designs that gave VMAX and then VMAX2 the title of “Most Reliable Symmetrix” ever produced.

VMAX3 is standing on the shoulders of the VMAX2 and is taking Reliability, Availability, and Serviceability to the next level.  Additional redundancy has been added on the backend DA connections to the DAEs.  More upgrades and service events leave the director online.  Tons of serviceability design changes for CS
have been added such as rear facing light bars (ever try to find one of these in a datacenter?), a work tray in the cabinets, etc. that set the VMAX3 as the RAS benchmark in the industry.

A Data Services Platform must be available, but it must also take functionality to the next level.  EMC’s goal for VMAX3 HyperMax OS is to make it the foundational component in our customer’s data center providing access to all of our best capabilities, FAST, SLO, FTS, Cloud, SRDF, TimeFinder, ProtectPoint, etc…).

HyperMax is our most comprehensive OS rewrite to date that takes the massively parallel preemptive multitasking kernel of VMAX3, and opens the door to running other data services apps inside the array.  The VMAX3 Data Services Platform can be extended with additional software functionality online as it comes available.  Most critically to our customers, code upgrades are done without taking a single component offline.  There is no failover/failback, one-at-a-time reboots, ports offline, etc. No other array can do this!

VMAX3 is designed for Always On operations.  Combined with SRDF, VPLEX + RecoverPoint, or ProtectPoint customer operations are always protected from outage, site failure, or any other outage situation.

We talk to our customers about “always on” Platform 3 infrastructure, and now we’ve built VMAX3 into an “always on” Data Services Platform.

VMAX3 Always On

Our phased release schedule enables us to get products to market faster.  We tell our customers how Agile development has revolutionized the way apps are brought to market and how the new normal is “fast.”   Get products out fast and iterate fast to
ramp up to provide the most important features first, prioritized by customer demand.  It’s one thing to do this in the Platform 3 web space, because the service is designed to be always on, and upgradable without degradation to the end user experience.  To do this in the hardware market, the platform must provide the same Always On availability to allow upgrades that enable the additional functionality.

In exactly this way, VMAX3 provides us a platform that is always available, and through HyperMax, additional data services can be added later online. Getting the base platform right is critical.   As previously discussed the upgradeability of VMAX3 allows us to introduce this revolutionary product now, which is crucial to maintaining market leadership position and building momentum while simultaneously creating a revolutionary new data services platform.

VMAX3 will carry customers toward Storage as a Service.  Fundamental to the value proposition of VMAX3, like VMAX Cloud Edition before it, is the idea that purchase decisions and provisioning are based on Service Level Objectives as opposed to rotational speed of the drives.  This outcome-based thinking is a wave carrying our customers toward the beachhead of Storage aaS.

Three years ago, EMC’s message for introducing ITaaS Transformation was 1) Have C-level sponsorship, 2) Pick a project and grow it, 3) Let the technology get you there.  Well, VMAX3 is getting our customers there.  Whether or not they have financial models that equate to the service levels inside VMAX3, they gain the simplicity and ability to automate processes that Service Level based provisioning provides.

The VMAX3 Data Services Platform can handle any performance Service Level Objective required by the customer.  Designed as an all-flash capable array, any of the VMAX3 family can support all-flash configurations.  Should the customer chose a higher capacity or lower cost design, spinning disk can be used in combination to provide various SLO’s of performance and capacity within the array.  Front-end and Back-end CPU cores are now pooled giving any port full line rate capability, doubling the IOPs capable on VMAX2.  PCI Gen 3 and 6Gb SAS connections to the DAE’s deliver incredible bandwidth for DSS workloads, tripling the throughput of VMAX2.  VMAX3 provides both the rich data services functionality and the performance required to process the avalanche of new data in the datacenter.

In summary, VMAX3 is an “always-on” scale-up-and-out Data Services Platform that can be online extended via HyperMax OS to provide additional advanced software functionality within the array over its lifespan with incredibly easy to use SLO based provisioning meeting any performance requirements of SAN or NAS attached hosts. This redefinition of the storage array makes VMAX3 a larger step forward from VMAX as VMAX was from DMX.

 

Size Does Matter

A colleague of mine Sean Cummins recently posted a very good summary of VMAX FAST VP best practices which got a little derision from some of the competition.

bitingoffMuch of the competitive argument is from those who sell an all flash array that can store about 100TB after deduplication. That’s a single RAID type.  That’s a single tier.  That’s a single price per GB.  That’s a single IO per GB. That’s great, but what about the other 90%+ of the capacity of the VMAX, and all of the other workloads that don’t require the highest of IOPS and lowest of latencies? EMC marketing has focused on the “optimization of flash” within the VMAX, but the flip side of that point-of-view is to drive costs out by a massive introduction of SATA without suffering the performance hit.

For the last several years, and for some time to come flash has been a means to an end. The real value of flash has been to enable the placement of as much capacity as possible onto cheap, high capacity, slow SATA. Customers can’t afford to place petabytes of data on all-flash solutions. There aren’t all-flash solutions that can accommodate that scale. The petabyte workloads have not required all-flash performance.

This to shall pass. Every major storage vendor has an all-flash product. There are even customers today (who shall not be named) that purchase all-flash VMAX! We’re starting to see how Latency is becoming the name of the game as opposed to IOPS. This will drive all-flash array consumption for those smaller workloads that can fit on an all-flash solution that don’t require enterprise federated consistency protection for BC/DR (that’s a whole other topic).

hammerThe world is changing rapidly. Data growth is exploding. Companies want to store everything in the hopes that they’ll be able to mine their data for customer insights. We’ll see what storage architectures emerge as the most effective for each of the various workloads in the world.  My warning is this. Whoever is selling you “only one way” of doing things is SELLING YOU on an idea that all of your workloads are the same. They have the proverbial hammer, and they’re trying to convince you all your problems are nails. Don’t buy it.

Community for Sharing Storage Resource Management (SRM) Custom Reports

EMC SRM 3.0I was in an EMC and EMC Partner training class around SRM 3.0 (Storage Resource Management Suite) today, and came away very excited about one of the latest software assets in EMC’s portfolio (SRM home page).  SRM 3.0 is a comprehensive tool for reporting on capacity and performance of the largest storage enterprises in the world.  Using passive or standards based active discovery, SRM can provide comprehensive capacity and performance reporting and trending across your entire IT stack: VM’s, physical servers, SAN infrastructure, and storage elements.

EMC is becoming very good at providing a simple and easy out-of-the-box experience for its software products while preserving a very rich underlying functionality and flexibility that elevates EMC products above the competition. This balance is very evident in SRM 3.0, where predefined active dashboards show summary data, and allow click-through to the underlying metrics and detailed reports. (Watch a demo video here).

SRM-ReportDuring the class lecture, (and the first hands-on experience I had with the tool) I was able to modify one of the standard storage pool reports to show the data reduction associated with Thin Provisioning on VMAX and VNX. I discussed some of these formulas in a previous post (Some EMC VMAX Storage Reporting Formulas).

One of the best parts of SRM 3.0 is the ability to export and import Report Templates. As such, I exported the report I customized, and posted the XML here for you to download and import into your own SRM 3.0 installation.

I’d like to invite all of you to export reports that you have customized for your environment with the community, so that we can engage the power of everyone to advance the art of storage resource management.

My first python and JSON code

I’m not a developer. There I said it.

I’m a presales technologist, architect, and consultant.

But I do have a BS in Computer Science. Way back there in my hind brain, there exists the ability to lay down some LOC.

I was inspired today by a peer of mine Sean Cummins (@scummins), another Principal within EMC’s global presales organization. He posted an internal writeup of connecting to the VMAX storage array REST API to pull statistics and configuration information. Did you know there is a REST API for a Symmetrix?! Sad to say most people don’t.

He got me hankering to try something, so I plunged into Python for the first time, and as my first example project, I attached to Google’s public geocoding API to map street addresses to Lat/Lng coordinates. (Since I don’t have a VMAX in the basement)

So here it is. I think it’s a pretty good first project to learn a few new concepts. I’ll figure out the best way to parse a JSON package eventually. Anyone have any advise?

###################
# Example usage
###################
$ ./geocode.py
Enter your address:  2850 Premiere Parkway, Duluth, GA                 
2850 Premiere Parkway, Duluth, GA 30097, USA is at
lat: 34.002958
lng: -84.092877

###################
# First attempt at parsing Google's rest api
###################
#!/usr/bin/python

import requests          # module to make html calls
import json          # module to parse JSON data

addr_str = raw_input("Enter your address:  ")

maps_url = "https://maps.googleapis.com/maps/api/geocode/json"
is_sensor = "false"      # do you have a GPS sensor?

payload = {'address': addr_str, 'sensor': is_sensor}

r = requests.get(maps_url,params=payload)

# store the json object output
maps_output = r.json()

# create a string in a human readable format of the JSON output for debugging
#maps_output_str = json.dumps(maps_output, sort_keys=True, indent=2)
#print(maps_output_str)

# once you know the format of the JSON dump, you can create some custom
# list + dictionary parsing logic to get at the data you need to process

# store the top level dictionary
results_list = maps_output['results']
result_status = maps_output['status']

formatted_address = results_list[0]['formatted_address']
result_geo_lat = results_list[0]['geometry']['location']['lat']
result_geo_lng = results_list[0]['geometry']['location']['lng']

print("%s is at\nlat: %f\nlng: %f" % (formatted_address, result_geo_lat, result_geo_lng))

Enterprise Storage Scale Out vs Scale Up

A lot of what we hear in the cloud washed IT conversation today is that scale out architectures are the preferred method of deployment over scale up designs. As the theory goes, scale out architectures provide more and more compute connectivity and storage capacity as you add nodes.

Scale outMost vendor marketing has jumped onto the scale out bandwagon leaving implementations that also scale up indistinguishable from the rest. Properly conceived scale out architectures allow us to add additional discrete workloads to an existing system by adding nodes to the underlying infrastructure. The additional nodes accommodate processing required to support the additional separate workloads. This kind of share nothing design, allows us to stack a large quantity of discrete workloads together and manage the entire system is one entity, even though it can be very hard to balance workload across the nodes (silo’ed).

But what about the large quantity of monolithic workloads that need scalable performance but are not able to be distributed across independent nodes? Take for example a single database instance that begins life with just a few users, and is supported well on a single node of a scale out architecture. As this instance grows, more users are added, the database grows beyond the confines of the single node. In many pure scale out implementations, adding additional nodes is ineffectual. The additional nodes do not share in the burden of the workload from the first node. They are designed merely to accommodate additional discrete workloads.

Scale upEMC scale out and up products such as VMAX Isilon, provide the cost benefits of a scale out model with the performance benefits of a scale up model. Purchasing only a few nodes initially, IT gains the cost benefit of starting small, and as workloads increase, additional nodes can be brought online, and data and workload is automatically redistributed across all nodes. This creates a share everything approach that supports the ability to scale up discrete applications and while also providing the ability to scale out stacking additional applications into the array along with the first. Software-based controls provide for multi-tenancy, tiering and performance optimization, workload priorities, and quality of service. This is as opposed to scale out architectures where rigid hardware defined partitions separate components of the solution.

I hope this helps demystify “scale out” a little. Just because you can manage a cluster of independent nodes, doesn’t mean it’s an integrated system. Unless workload tasks are shared across the entire resource pool, scale out does not confer the benefits of scale up, where scale up-and-out designs provide the best of both worlds.

Some EMC VMAX Storage Reporting Formulas

What goes around comes around. Back in the day everything was done on the command line. Storage administrators had their scripts, their hands never had to leave the keyboard, they could provision storage quickly and efficiently, and life was good. Then the age of the easy to use GUI came into effect. Non-storage administrators could deploy their own capacity, and vendors espouse how much easier and more effective IT generalists could be in managing multiple petabytes of capacity with the mouse. Now we’re into the age of the API. Storage along with all other IT resource domains has its reporting API and developers are now writing code to automatically report on and provision storage in the enterprise. And many shops have realized that the command line is as effective an API for automating reports and doing rapid deployments as any REST based API could be.

So I’m getting a lot of questions again how to do some basic reporting on a VMAX storage array from command line generated or API generated data. Customers are writing their own reporting dashboards to support multiple frameworks.

The most fundamental question is how much of my storage array have I consumed? Assuming one is inquiring about capacity and not performance, there are two aspects to that question. The first question is from the business, “How much capacity has been allocated to my hosts?” In other words how much capacity do my hosts perceive they have been assigned? In an EMC VMAX array this has to do with the Subscription rate. For better or for worse we provide a Subscription percentage of a pool that has been Subscribed, as opposed to an absolute value of capacity.

To calculate how much of your array has been Subscribed, sum the products of the Pool % Subscribed by Pool Total Available GB to get the total capacity Subscribed to the hosts.

Symcff list thin pools detail

 

EFD Pool Subs % * EFD Pool Total GBs = EFD Subscribed GB
FC Pool Subs % * FC Pool Total GB = FC Subscribed GB
SATA Pool Subs % * SATA Pool Total GB = SATA Subscribed GB
Sum the totals above = Array Subscribed GB

Using the example above, this would be:

0% * 4,402.4 = 0.0  EFD Subscribed GB
177% * 77,297.4 = 136,816.398 FC Subscribed GB
73% * 157,303.7 = 114,831.701 SATA Subscribed GB
251,648.099 Array Subscribed GB

Then compare that number to the sum of the Total GBs provided by each of your three pools to reveal the ratio of the subscribed array capacity versus the total available capacity.

If you’re Array Subscribed GB is larger than your Array Total GB, then tread carefully since you have oversubscribed your array’s capacity!

Subscribed GB / Array Total GB = Array Subscription Rate
251,648.099 / 239,003.5 = 105.29% oversubscribed in this case!

To understand how much capacity has been filled physically allocated on the array after all data reduction technologies have done their job (thin provisioning, compression, de-dupe), simply sum the Used GBs capacity from each of your pools to show an overall array consumed capacity. Compare this number to the previously mentioned subscription capacity to show the benefit of data reduction technologies in the array.

Sum of Used GBs above is ( 3855.6 + 38708.4 + 43987.6 ) = 86,551.6 GB
251,648.099 Subscribed GB / 86,551.6 Consumed GB = 2.9 : 1 Data Reduction provided by Thin Provisioning!  Not bad at all!

Likewise sum the free capacity measures from each pool together to get the available capacity remaining in the array.

There you have it. If you’re not into the GUI, just a little math provide you the dashboard data you need to show your executives the capacity utilization of the array.

Happy hacking!

Array Sizing based on ITaaS Principles

There are two hats we wear when creating an IT service offering.  The first is the customer-facing marketing hat where customer needs are defined, and service offerings are advertised at various price levels in a catalog.  The second hat is the backend product management hat where the front-facing service catalog is translated to various reference architectures at well defined cost levels.

The goal when running IT as a business (aka: ITaaS) is that the fully burdened costs associated with the reference architecture are recouped over a reasonable period of time by the pricing set in the service catalog.

A few rules to live by in this world view related to enterprise storage:

  • The customer (business user) only has visibility to the Service Catalog.  All communications with the customer are based on the language used in the Catalog.
  • The customer (business user) never sees the Reference Architecture that supports each of the Services listed in the Catalog.  Never have customer communications about arrays, drive mix, RAID type, methods, processes, etc.
  • If the customer is unhappy with the service they are receiving, an evaluation must be made as to whether or not they are receiving the expected Service Level Objective as defined in the Service Catalog.  If not… it’s IT’s problem.  If they are and are still unhappy, then a discussion must follow about moving them to a different Tier of service as defined in the Service Catalog.
  • If a differentiated Tier of service does not exist in the Service Catalog, and there is a defined customer need for that class of service, then an end-to-end service definition must occur.  IT must create and price a new offering in the Service Catalog.  IT must also create a corresponding new definition in the Reference Architecture to explain how that service will be accomplished and at what cost.

What does this have to do with sizing a storage array?

Whether or not IT is living out the utopian vision of ITaaS, some concrete realities emerge from the ITaaS vision that show up in array sizing, namely:  Subscribed vs. Allocated capacity planning, and performance planning measured in IOPS/GB.

Warning:  There are lots of different considerations when it comes to array performance:  Write%, Skew, Cache Hit Rate, Sequentiality, Block Size, app sensitivity to Latency vs. Throughput, advanced data protection services etc.  Let’s take these for granted for now and focus on IOPS/GB, because frankly that’s how the Service Providers do it.

How much capacity do I need to purchase?

Customer facing Service Catalog capacity planning should start with the Subscribed capacity.  This defines the promise IT makes to the user about how much capacity is available to store data and at what tier. This is the advertised capacity to which pricing is set.

IT internal Reference Architecture capacity planning should worry more about the Consumed capacity after any available data reduction technologies do their work (thin, de-dupe, compress, etc).  This is the actual capacity (plus buffer) to be purchased to which costs are allocated.

What kind of performance do I need?

Customer facing Service Catalog performance planning can be done by SLA or SLO.  Do you want to establish a worst case scenario floor that you promise to exceed every time (SLA)?  Or do you establish a best case performance target ceiling you promise to try and deliver (SLO)?  The world is moving away from SLA’s to SLO’s.  The IOPS/GB performance level is defined as the IOPS to the Host divided by the Subscribed GB (IOPS / GBs).

IT internal Reference Architecture performance planning should be done against IOPS to the Host divided by the Thin Consumed GB (IOPS / GBc).

An IOPS / GB sizing example that leads to poor performance

A Business customer has an existing application in a legacy storage environment.  They’ve got 40TB of capacity assigned and are doing about 4,000 IOPS to the array.

That’s 4,000 / 40,000 = .1 IOPS/GBs (Host IOPS per Subscribed GigaByte).

A purchase is made for 56 x 1TB SATA drives RAID 6 6+2 providing ~40TB capacity, and  4,480 IOPS (56 * 80 IOPS) to fit the .1 IOPS/GB workload profile rather well (ignoring too many factors).

But … after Thin Provisioning, only 40% of the capacity is consumed (16TB), and the IT Finance Dept says, wow, I’ve still got another 34TB of capacity available.  Let’s use it!!  The problem is, they’ve consumed all their IOPS, leaving a lot of capacity stranded and unable to be used.

An IOPS / GB sizing example that leads to good performance

A Business customer has an existing application in a legacy storage environment.  They’ve got 40TB of capacity assigned and are doing about 4,000 IOPS to the array.  Planning for Thin Provisioning, we expect only the first 40% (16TB) will actually be Consumed.

That’s 4,000 / (40,000 * 40%) = .25 IOPSh/GBc (Host IOPS per Consumed GigaByte).

A purchase is made for 42 x 600GB 10K drives RAID 5 3+1 providing ~17TB capacity, and  ~5,000 IOPS (42 x 120) to fit the workload profile rather well (ignoring many factors, see “Disk IOPS” discussion further down).  This also assumes the risk that Thin Provisioning or other data reduction technologies are able to do their job effectively and allow you to purchase less capacity than in a “thick” world.

Over time, the app performs well, and is more properly balanced in both the IOPS and TB dimensions.

What if your boss says you must purchase the Thick capacity?

Some pointy haired bosses who don’t trust that Thin Provisioning will provide real benifit will say that you must purchase the “Thick” capacity amount due to various sociological / political / economic conditions within your organization.

In this case, make two assumptions:

  1. Your goal is to reach 80% capacity consumed over the life of your array
  2. Thin will cause 40% Consumption of every Subscribed GB

Use the formula Host IOPS  / ( Thick GB * 40% ) to arrive at your target IOPS/GB for the entire array’s worth of capacity!  This will be a much bigger  number than before, and will equate to a much higher performance solution.  The trick is, when Thin Provisioning (or other data reduction) kicks in, you’ll have so much head room, that someone will want to stack additional applications onto the array to consume all the capacity.

You’ll need the additional performance to accommodate this inevitable consolidation.

Use the above calculated IOPS/GB * (Thick GB * 80%) to reveal the target Host IOPS needed to achieve full efficient use of the installed capacity.  It’s never a good idea to consume more than 80% of a presumably over-subscribed resource.  It’s ok to target your performance to equate to 80% capacity consumption.

Let’s add RAID Type and Read/Write % into the mix

So far, I’ve been focused on “Host IOPS” when discussing IO Density.  Now that we’ve calculated the Host IOPS and Capacity properly, how do we configure an array to achieve a certain Host IOPS Density?  We need to translate Host IOPS down into “Disk IOPS.”
By Disk IOPS, I mean the amount of work the underlying Flash or HDD Spindles must do to accomplish the Host IOPS goal.  I won’t have room to discuss all of the potential factors that go into a model like this, but we’ll get started down the path.

FYI, some of those factors are: block size, cache benefits, data reduction (benefit or overhead), replication, etc.

Most implementations of traditional RAID types RAID-1, RAID-5, and RAID-6 carry with them some form of write IO penalty. When a host writes to a RAID-1 mirror device, 2 IOs are generated by the backend storage representing the write to the first spindle, and the second write to the mirror pair.  So we say that the write penalty for RAID-1 is 2.  When a host writes to a RAID-5 protected device and the write I/O is not the full stripe width, then potentially 4 IOs must happen on the backend of the array. One read I/O from the parity portion, a second read I/O from the data stripe, CRC calculations are done in memory, and then a third IO is issued to write the parity portion, and the fourth and last IO is issued to write the data stripe. So we say that RAID-5 has a 4 IO write penalty. For RAID-6, there must be a read of the first parity, a read of the second parity, a read of the data stripe, and after CRC calculations are done three writes must occur to finish the IO. So we say that RAID-6 carries a 6 IO write penalty.

Coupled with RAID type, we must know the read/write percentage of our workload. We assume that reads do not carry any penalty to the backend spindles, so for every host read there is a single disk I/O.  Obviously at this point we are not factoring in any cache benefits.  We must multiply every host write by the write IO penalty previously described. So knowing that we need to support a 10,000 IO workload which is 60% writes, we can calculate how many IOPS the backend disks must be able to provide.

IOPS to Disk  = %READS * Host-IOPS + %WRITES * Host-IOPS *  RAID-Penalty

Where the RAID-Penalty is 2, 4, or 6 as described above.

Now that we know how many disk IOPS our spindles must be able to produce, how many spindles do we need? Use the following chart to reference how many IOPS per spindle technology type can be provided with no more than one outstanding queued I/O to the spindle (aka:  good response times).

2500 IOPS per EFD (200GB EFD = 12.5 IOPS/GB)
180 IOPS per 15K RPM spindle (2.5″ or 3.5″ doesn’t matter)  (300GB = .6 IOPS/GB)
120 IOPS per 10K RPM spindle (2.5″ or 3.5″ doesn’t matter)  (600GB = .2 IOPS/GB)
80 IOPS per 7.2K RPM spindle (typically SATA)  (2TB = .04 IOPS/GB)

A Final Example

A Business customer has an existing application in a legacy storage environment.  They’ve got 40TB of capacity assigned and are doing about 4,000 IOPS to the array.  Planning for Thin Provisioning, we expect only the first 40% (16TB) will actually be Consumed.  Our pointy haired boss is going to require that we purchase all 40TB of capacity… hmph.

That’s 4,000 / (40,000 * 40%) = .25 IOPSh/GBc (Host IOPS per Consumed GigaByte).

Now factoring in the inevitable growth that we know will happen due to all of the free space that will remain in the array, we say:

.25 IOPS/GB * (40,000 * 80%) = 8,000 Host IOPS that the array solution will need to support over it’s lifespan

Ignoring the complexity of multi-tiered solutions, let’s look for a single spindle type that will meet our needs.  Our IOPS/GB target is .25.  From the previous table, it looks like a 600GB 10K is .2 and a 300GB 15K is .6… Hmmm, what about a 600GB 15K drive?  That would be:

180 IOPS / 600GB = .3 IOPS/GB  It looks like a match!

Since this is a consolidated general purpose VMware farm that we’re supporting, let’s assume the Read/Write ratio will be 40% Reads and 60% Writes (this is VERY consistent across VMware clusters, FYI).  Also, since we’re using fast FC spindles, let’s see what a RAID-5 3+1 solution will look like, shall we?

Here’s our formula repeated from above:
IOPS to Disk  = %READS * Host-IOPS + %WRITES * Host-IOPS *  RAID-Penalty

40% * 8,000 + 60% * 8,000 * 4 = 22,400 IOPS to Disk
Wow, that’s a lot of write penalty, since there we only needed 8,000 Host IOPS.  Our 600GB 15K RPM drive can do 180 IOPS per spindle, so 22,400 / 180 = ~124 spindles… let’s round up to 128 since that’s a nice power of 2 to achieve the performance needed to support our hosts.

Now, does this provide enough capacity?  Well, 128 * 600GB * 3/4 (RAID Overhead) = 57,600 GB.  It looks like we’ve over-shot our capacity numbers a little.  Perhaps a 300GB drive would be more efficient?  Maybe a 300GB 10K?  Maybe we should look at a 3 tier approach with a little EFD, some FC, and most of the capacity in SATA?

And this dear reader is an exercise for you 😉

Take this to the extreme with all flash arrays

Always on in-line deduplication provided by quality All Flash Arrays take this notion to the extreme.  These arrays don’t allocate anything unless it’s globally unique data.  A single EMC XtremIO X-brick provides 7.5TB of usable capacity with 70TB of logical de-duped capacity.  This 10X increase in the potential IOPS/GB density requires Flash at the core and innovative workload management algorithms to enable extreme performance that’s hitting a very limited set of allocated data regions.

What about the benefits of cache?

It is a dangerous and complex game to try and figure out the benefit of cache in any enterprise storage array. Cache serves two purposes. First, cache provides a buffer to accept spikes in host generated workload for asynchronous destage to the backend media post acknowledgment to the host. This function provides good latency to the host while leveling out the workload characteristics seen by the backend spindles.  This does not necessarily reduce the amount of work that the spindles must do overall.  The second function of cache is to legitimately reduce the amount of work the backend spindles must accomplish. This is done by caching host writes for subsequent reuse, and by being a prefetch buffer for reads that are predicted for near future use.  In my experience prefetched reads reduce backend spindle work to about 30% of what it would normally be.  Accommodating host rewrites to active cached data before it is destaged to the backend saves the spindles from having to do any of that work.  The question is how do you predict the amount of read cache hits, read cache misses, prefetched reads, writes to backend, and re-writes from any given host workload?  These arrays specific implementation details that depend on your specific host workload profile, and make it very dangerous to assume much cache benefit.  Just let it come naturally and be the icing on the cake of your good performing solution.