Hardware vs. software innovation – round 4

We, the industry and I, have had a long running debate on whether hardware innovation still makes sense anymore (see my Hardware vs. software innovation – rounds 1, 2, & 3 posts).

The news within the last week or so is that Dell-EMC cancelled their multi-million$, DSSD project, which was a new hardware innovation intensive, Tier 0 flash storage solution, offering 10 million of IO/sec at 100µsec response times to a rack of servers.

DSSD required specialized hardware and software in the client or host server, specialized cabling between the client and the DSSD storage device and specialized hardware and flash storage in the storage device.

What ultimately did DSSD in, was the emergence of NVMe protocols, NVMe SSDs and RoCE (RDMA over Converged Ethernet) NICs.

Last weeks post on Excelero (see my 4.5M IO/sec@227µsec … post) was just one example of what can be done with such “commodity” hardware. We just finished a GreyBeardsOnStorage podcast (GreyBeards podcast with Zivan Ori, CEO & Co-founder, E8 storage) with E8 Storage which is yet another approach to using NVMe-RoCE “commodity” hardware and providing amazing performance.

Both Excelero and E8 Storage offer over 4 million IO/sec with ~120 to ~230µsec response times to multiple racks of servers. All this with off the shelf, commodity hardware and lots of software magic.

Lessons for future hardware innovation

What can be learned from the DSSD to NVMe(SSDs & protocol)-RoCE technological transition for future hardware innovation:

  1. Closely track all commodity hardware innovations, especially ones that offer similar functionality and/or performance to what you are doing with your hardware.
  2. Intensely focus any specialized hardware innovation to a small subset of functionality that gives you the most bang, most benefits at minimum cost and avoid unnecessary changes to other hardware.
  3. Speedup hardware design-validation-prototype-production cycle as much as possible to get your solution to the market faster and try to outrun and get ahead of commodity hardware innovation for as long as possible.
  4. When (and not if) commodity hardware innovation emerges that provides  similar functionality/performance, abandon your hardware approach as quick as possible and adopt commodity hardware.

Of all the above, I believe the main problem is hardware innovation cycle times. Yes, hardware innovation costs too much (not discussed above) but I believe that these costs are a concern only if the product doesn’t succeed in the market.

When a storage (or any systems) company can startup and in 18-24 months produce a competitive product with only software development and aggressive hardware sourcing/validation/testing, having specialized hardware innovation that takes 18 months to start and another 1-2 years to get to GA ready is way too long.

What’s the solution?

I think FPGA’s have to be a part of any solution to making hardware innovation faster. With FPGA’s hardware innovation can occur in days weeks rather than months to years. Yes ASICs cost much less but cycle time is THE problem from my perspective.

I’d like to think that ASIC development cycle times of design, validation, prototype and production could also be reduced. But I don’t see how. Maybe AI can help to reduce time for design-validation. But independent FABs can only speed the prototype and production phases for new ASICs, so much.

ASIC failures also happen on a regular basis. There’s got to be a way to more quickly fix ASIC and other hardware errors. Yes some hardware fixes can be done in software but occasionally the fix requires hardware changes. A quicker hardware fix approach should help.

Finally, there must be an expectation that commodity hardware will catch up eventually, especially if the market is large enough. So an eventual changeover to commodity hardware should be baked in, from the start.


In the end, project failures like this happen. Hardware innovation needs to learn from them and move on. I commend Dell-EMC for making the hard decision to kill the project.

There will be a next time for specialized hardware innovation and it will be better. There are just too many problems that remain in the storage (and systems) industry and a select few of these can only be solved with specialized hardware.


Picture credit(s): Gravestones by Sherry NelsonMotherboard 1 by Gareth Palidwor; Copy of a DSSD slide photo taken from EMC presentation by Author (c) Dell-EMC

Crowdsourcing made better

765140960_735722ddf8_zRead an article the other day in MIT News (Better wisdom from crowds) about a new approach to drawing out better information from crowdsourced surveys. It’s based on something the researchers have named the “surprising popularity” algorithm.

Normally, when someone performs a crowdsourced survey, the results of the survey are typically some statistically based (simple or confidence weighted) average of all the responses. But this may not be correct because, if the majority are ill-informed then any average of their responses will most likely be incorrect.

Surprisingly popular?

10955401155_89f0f3f05a_zWhat surprising popularity does, is it asks respondents what they believe will be the most popular answer to a question and then asks what the respondent believes the correct answer to the question. It’s these two answers that they then use to choose the most surprisingly popular answer.

For example, lets say the answer the surveyors are looking for is the capital of Pennsylvania (PA, a state in the eastern USA) Philadelphia or not. They ask everyone what answer would be the most popular answer. In this case yes, because Philadelphia is large and well known and historically important. But they then ask for a yes or no on whether Philadelphia is the capital of PA. Of course the answer they get back from the crowd here is also yes.

But, a sizable contingent would answer that the capital of PA is  Philadelphia wrong (it is actually Harisburg). And because there’s a (knowledgeable) group that all answers the same (no) this becomes the “surprisingly popular” answer and this is the answer the surprisingly popular algorithm would choose.

What it means

The MIT researchers indicated that their approach reduced errors by 21.3% over a simple majority and 24.2% over a confidence weighted average.

What the researchers have found, is that surprisingly popular algorithm can be used to identify a knowledgeable subset of individuals in the respondents that knows the correct answer.  By knowing the most popular answer, the algorithm can discount this and then identify the surprisingly popular (next most frequent) answer and use this as the result of the survey.

Where might this be useful?

In our (USA) last election there were quite a few false news stories that were sent out via social media (Facebook and Twitter). If there were a mechanism to survey the readers of these stories that asked both whether this story was false/made up or not and asked what the most popular answer would be, perhaps the new story truthfulness could be completely established by the crowd.

In the past, there were a number of crowdsourced markets that were being used to predict stock movements, commodity production and other securities market values. Crowd sourcing using surprisingly popular methods might be used to better identify the correct answer from the crowd.

Problems with surprisingly popular methods

The one issue is that this approach could be gamed. If a group wanted some answer (lets say that a news story was true), they could easily indicate that the most popular answer would be false and then the method would fail. But it would fail in any case if the group could command a majority of responses, so it’s no worse than any other crowdsourced approach.


Photo Credit(s): Crowd shot by Andrew WestLost in the crowd by Eric Sonstroem


Hitachi and the coming IoT gold rush

img_7137Earlier this week I attended Hitachi Summit 2016 along with a number of other analysts and Hitachi executives where Hitachi discussed their current and ongoing focus on the IoT (Internet of Things) business.

We have discussed IoT before (see QoM1608: The coming IoT tsunami or not, Extremely low power transistors … new IoT applications). Analysts and companies predict  ~200B IoT devices by 2020 (my QoM prediction is 72.1B 0.7 probability). But in any case there’s a lot of IoT activity going to come online, very shortly. Hitachi is already active in IoT and if anything, wants it to grow, significantly.

Hitachi’s current IoT business

Hitachi is uniquely positioned to take on the IoT business over the coming decades, having a number of current businesses in industrial processes, transportation, energy production, water management, etc. Over time, all these industries and more are becoming much more data driven and smarter as IoT rolls out.

Some metrics indicating the scale of Hitachi’s current IoT business, include:

  • Hitachi is #79 in the Fortune Global 500;
  • Hitachi’s generated $5.4B (FY15) in IoT revenue;
  • Hitachi IoT R&D investment is $2.3B (over 3 years);
  • Hitachi has 15K customers Worldwide and 1400+ partners; and
  • Hitachi spends ~$3B in R&D annually and has 119K patents

img_7142Hitachi has been in the OT (Operational [industrial] Technology) business for over a century now. Hitachi has also had a very successful and ongoing IT business (Hitachi Data Systems) for decades now.  Their main competitors in this IoT business are GE and Siemans but neither have the extensive history in IT that Hitachi has had. But both are working hard to catchup.

Hitachi Rail-as-a-Service

img_7152For one example of what Hitachi is doing in IoT, they have recently won a 27.5 year Rail-as-a-Service contract to upgrade, ticket, maintain and manage all new trains for UK Rail.  This entails upgrading all train rolling stock, provide upgraded rail signaling, traffic management systems, depot and station equipment and ticketing services for all of UK Rail.

img_7153The success and profitability of this Hitachi service offering hinges on their ability to provide more cost efficient rail transport. A key capability they plan to deliver is predictive maintenance.

Today, in UK and most other major rail systems, train high availability is often supplied by using spare rolling stock, that’s pre-positioned and available to call into service, when needed. With Hitachi’s new predictive maintenance capabilities, the plan is to reduce, if not totally eliminate the need for spare rolling stock inventory and keep the new trains running 7X24.

img_7145Hitachi said their new trains capture 48K data items and generate over ~25GB/train/day. All this data, will be fed into their new Hitachi Insight Group Lumada platform which includes Pentaho, HSDP (Hitachi Streaming Data Platform) and their Content Analytics to analyze train data and determine how best to keep the trains running. Behind all this analytical power will no doubt be HDS HCP object store used to keep track of all the train sensor data and other information, Hitachi UCP servers to process it all, and other Hitachi software and hardware to glue it all together.

The new trains and services will be rolled out over time, but there’s a pretty impressive time table. For instance, Hitachi will add 120 new high speed trains to UK Rail by 2018.  About the only thing that Hitachi is not directly responsible for in this Rail-as-a-Service offering, is the communications network for the trains.

Hitachi other IoT offerings

Hitachi is actively seeking other customers for their Rail-as-a-service IoT service offering. But it doesn’t stop there, they would like to offer smart-water-as-a-service, smart-city-as-a-service, digital-energy-as-a-service, etc.

There’s almost nothing that Hitachi currently supplies as industrial products that they wouldn’t consider offering in an X-as-a-service solution. With HDS Lumada Analytics, HCP and HDS storage systems, Hitachi UCP converged infrastructure, Hitachi industrial products, and Hitachi consulting services, together they are primed to take over the IoT-industrial products/services market.

Welcome to the new Hitachi IoT world.


Flash’s only at 5% of data storage

7707062406_6508dba2a4_oWe have been hearing for years that NAND flash is at price parity with disk. But at this week’s Flash Memory Summit, Darren Thomas, VP Storage BU, Micron said at his keynote that NAND only store 5% of the bits in a data center.

Darren’s session was all about how to get flash to become more than 5% of data storage and called this “crossing the chasm”. I assume the 5% is against yearly data storage shipped.

Flash’s adoption rate

Darren, said last year flash climbed from 4% to 5% of data center storage, but he made no mention on whether flash’s adoption was accelerating. According to another of Darren’s charts, flash is expected to ship ~77B Gb of storage in 2015 and should grow to about 240B Gb by 2019.

If the ratio of flash bits shipped to data centers (vs. all flash bits shipped) holds constant then Flash should be ~15% of data storage by 2019. But this assumes data storage doesn’t grow. If we assume a 10% Y/Y CAGR for data storage, then flash would represent about ~9% of overall data storage.

Data growth at 10% could be conservative. A 2012 EE Times article said2010-2015 data growth CAGR would be 32%  and IDC’s 2012 digital universe report said that between 2012 and 2020, data will double every two years, a ~44% CAGR. But both numbers could be talking about the world’s data growth, not just data center.

How to cross this chasm?

Geoffrey Moore, author of Crossing the Chasm, came up on stage as Darren discussed what he thought it would take to go beyond early adopters (visionaries) to early majority (pragmatists) and reach wider flash adoption in data center storage. (See Wikipedia article for a summary on Crossing the Chasm.)

As one example of crossing the chasm, Darren talked about the electric light bulb. At introduction it competed against candles, oil lamps, gas lamps, etc. But it was the most expensive lighting system at the time.

But when people realized that electric lights could allow you to do stuff at night and not just go to sleep, adoption took off. At that time competitors to electric bulb did provide lighting it just wasn’t that good and in fact, most people went to bed to sleep at night because the light then available was so poor.

However, the electric bulb  higher performing lighting solution opened up the night to other activities.

What needs to change in NAND flash marketing?

From Darren’s perspective the problem with flash today is that marketing and sales of flash storage are all about speed, feeds and relative pricing against disk storage. But what’s needed is to discuss the disruptive benefits of flash/NAND storage that are impossible to achieve with disk today.

What are the disruptive benefits of NAND/flash storage,  unrealizable with disk today.

  1. Real time analytics and other RT applications;
  2. More responsive mobile and data center applications;
  3. Greener, quieter, and potentially denser data center;
  4. Storage for mobile, IoT and other ruggedized application environments.

Only the first three above apply  to data centers. And none seem as significant  as opening up the night, but maybe I am missing a few.

Also the Wikipedia article cited above states that a Crossing the Chasm approach works best for disruptive or discontinuous innovations and that more continuous innovations (doesn’t cause significant behavioral change) does better with Everett Roger’s standard diffusion of innovation approaches (see Wikepedia article for more).

So is NAND flash a disruptive or continuous innovation?  Darren seems firmly in the disruptive camp today.


Photo Credit(s): 20-nanometer NAND flash chip, IntelFreePress’ photostream

Moore’s law is still working with new 2D-electronics, just 1nm thin

ncomms8749-f1This week scientists at Oak Ridge National Laboratory have created two dimensional nano-electronic circuits just 1nm tall (see Nature Communications article). Apparently they were able to create one crystal two crystals ontop of one another, then infused the top that layer with sulfur. With that as a base they used  standard scalable photolitographic and electron beam lithographic processing techniques to pattern electronic junctions in the crystal layer and then used a pulsed laser evaporate to burn off selective sulfur atoms from a target (selective sulferization of the material), converting MoSe2 to MoS2. At the end of this process was a 2D electronic circuit just 3 atoms thick, with heterojunctions, molecularly similar to pristine MOS available today, but at much thinner (~1nm) and smaller scale (~5nm).

In other news this month, IBM also announced that they had produced working prototypes of a ~7nm transistor in a processor chip (see NY Times article). IBM sold off their chip foundry a while ago to Global Foundries, but continue working on semiconductor research with SEMATECH, an Albany NY semiconductor research consortium. Recently Samsung and Intel left SEMATECH, maybe a bit too early.

On the other hand, Intel announced they were having some problems getting to the next node in the semiconductor roadmap after their current 14nm transistor chips (see Fortune article).  Intel stated that the last two generations took  2.5 years instead of 2 years, and that pace is likely to continue for the foreseeable future.  Intel seems to be spending more research and $’s creating low-power or new (GPUs) types of processing than in a mad rush to double transistors every 2 years.

480px-Comparison_semiconductor_process_nodes.svgSo taking it all in, Moore’s law is still being fueled by Billion $ R&D budgets and the ever increasing demand for more transistors per area. It may take a little longer to double the transistors on a chip, but we can see at least another two generations down the ITRS semiconductor roadmap. That is, if the Oak Ridge research proves manufacturable as it seems to be.

So Moore’s law has at least another generation or two to run. Whether there’s a need for more processing power is anyone’s guess but the need for cheaper flash, non-volatile memory and DRAM is a certainty for as far as I can see.


Photo Credits: 

  1. From “Patterned arrays of lateral heterojunctions within monolayer two-dimensional semiconductors”, by Masoud Mahjouri-Samani, Ming-Wei Lin, Kai Wang, Andrew R. Lupini, Jaekwang Lee, Leonardo Basile, Abdelaziz Boulesbaa, Christopher M. Rouleau, Alexander A. Puretzky, Ilia N. Ivanov, Kai Xiao, Mina Yoon & David B. Geohegan
  2. From Comparison semiconductor process nodes” by Cmglee – Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Comparison_semiconductor_process_nodes.svg#/media/File:Comparison_semiconductor_process_nodes.svg

Storage systems on Agile

640px-Scrum_FrameworkWas talking with Qumulo‘s CEO Peter Godman earlier this week for another GreyBeards On Storage Podcast (not available yet). One thing he said which was hard for me to comprehend was that they were putting out a new storage software release every 2 weeks.

Their customers are updating their storage system software every 2 weeks.

In my past life as a storage systems development director, we would normally have to wait months if not quarters before customers updated their systems to the latest release. As a result, we strived to put out an update at most, once a quarter with a major release every year to 18 months or so.

To me releasing code to the field every two weeks sounds impossible or at best very risky. Then I look at my iPhone. I get updates from Twitter, Facebook, LinkedIN and others, every other week. And Software-as-a-service (SaaS) solutions often update their systems frequently, if not every other week. Should storage software be any different?

It turns out Peter and his development team at Qumulo have adopted SaaS engineering methodology, which I believe uses Agile development.

Agile development

As I understand it Agile development has a couple of tenets (see Wikipedia article for more information):

  • Individuals and interaction – leading to co-located teams, with heavy use of pair programming, and developer generated automated testing, rather than dispersed teams with developers and QA separate but (occasionally) equal.
  • Working software – using working software as a means of validating requirements, showing off changes and modifying code rather than developing reams of documentation.
  • (Continuous) Customer collaboration – using direct engagement with customers over time to understand changes (using working software) rather than one time contracts for specifications of functionality
  • Responding to change – changing direction in real time using immediate customer feedback rather than waiting months or a year or more to change development direction to address customer concerns.

In addition to all the above, Agile development typically uses Scrum for product planning. An Agile Scrum (see picture above & Wikipedia article) is a weekly (maybe daily) planning meeting, held standing up and discussing what changes go into the code next.

This is all fine for application development which involves a few dozen person years of effort but storage software development typically takes multiple person centuries of development & QA effort. In my past life, our storage system regression testing typically took 24 hours or more and proper QA validation took six months or more of elapsed time with ~ 5 person years or so of effort, not to mention beta testing the system at a few, carefully selected customer sites for 6 weeks or more. How can you compress this all into a few weeks?

Software development on Agile

With Agile, you probably aren’t beta testing a new release for 6 weeks anywhere, anymore. While you may beta test a new storage system for a period of time you can’t afford the time to do this on subsequent release updates anymore.

Next, there is no QA. It’s just a developer/engineer and their partner. Together they own code change and its corresponding test suite. When one adds functionality to the system, it’s up to the team to add new tests to validate it. Test automation helps streamline the process.

Finally, there’s continuous integration to the release code in flight. Used to be a developer would package up a change, then validate it themselves (any way they wanted), then regression test it integrated with the current build, and then if it was deemed important enough, it would be incorporated into the next (daily) build of the software. If it wasn’t important, it could wait on the shelf (degenerating over time due to lack of integration) until it came up for inclusion. In contrast, I believe Agile software builds happen hourly or even more often (in real time perhaps), changes are integrated as soon as they pass automated testing, and are never put on the shelf. Larger changes may still be delayed until a critical mass is available, but if it’s properly designed even major changes can be implemented incrementally. Once in the build, automated testing insures that any new change doesn’t impact working functionality.

Due to the length of our update cycle, we often had 2 or more releases being validated at any one time. Unclear to me whether Agile allows for multiple releases in flight as it just adds to the complexity and any change may  have to be tailored for each release it goes into.

Storage on Agile

Vendors are probably not doing this with hardware that’s also undergoing significant change. Trying to do both would seem suicidal.

Hardware modifications often introduce timing alterations that can expose code bugs that had never been seen before. Hardware changes also take a longer time to instantiate (build into electronics). This can be worked around by using hardware simulators but timing is often not the same as the real hardware and it can take 10X to 100X more real-time to execute simple operations. Nonetheless, new hardware typically takes weeks to months to debug and this can be especially hard if the software is changing as well.

Similar to hardware concerns, OS or host storage protocol changes (say from NFSv3 to NFSv4) would take a lot more testing/debugging to get right.

So it helps if the hardware doesn’t change, the OS doesn’t change and the host IO protocol doesn’t change when your using Agile to develop storage software.

The other thing that we ran into is that over time, regression testing just kept growing and took longer and longer to complete. We made it a point of adding regression tests to validate any data loss fix we ever had in the field. Some of these required manual intervention (such as hardware bugs that need to be manually injected). This is less of a problem with a new storage system and limited field experience, but over time fixes accumulate and from a customer perspective, tests validating them are hard to get rid of.

Hardware on Agile

Although a lot of hardware these days is implemented as ASICs, it can also be implemented via Field Programmable Gate Arrays (FPGAs). Some FPGAs can be configured at runtime (see Wikipedia article on FPGAs), that is in the field, almost on demand.

FPGA programming is done using a hardware description language, an electronic logic coding scheme. It looks very much like software development of hardware logic. Why can’t this be incrementally implemented, continuously integrated, automatically validated and released to the field every two weeks.

As discussed above, the major concern is that new hardware introducing timing changes which expose hard to find (software and hardware) bugs.

And incremental development of original hardware, seems akin to having a building’s foundation changing while your adding more stories. One needs a critical mass of hardware to get to a base level of functionality to run storage functionality. This is less of a problem when one’s adding or modifying functionality for current running hardware.


I suppose Qumulo’s use of Agile shouldn’t be much of a surprise. They’re a startup, with limited resources, and need to play catchup with a lot of functionality to implement. It’s risky from my perspective but you have to take calculated risks if your going to win the storage game.

You have to give Qumulo credit for developing their storage using Agile and being gutsy enough to take it directly to the field. Let’s hope it continues to work for them.

Photo Credits“Scrum Framework” by Source (WP:NFCC#4). Licensed under Fair use via Wikipedia

EMCworld 2013 day 1

Lines for coffee at the Cafe were pretty long this morning and I missed my opportunity to have breakfast to do some work. But eventually made my way to the press room and got some food and coffee.

Spent the morning in Analyst sessions mostly under NDA but it seems safe to say that EMC sees plenty of opportunity ahead.

The first session Q&A with BRS executives and customers was enlightening but the main message from the customers was that data protection is hard, legacy systems often can’t adjust quick enough and sometimes a completely new architecture is warranted. The executives were upbeat about current BRS business and where they were headed in the future.

20130506-142735.jpgRest of the morning was with Jeremy Burton EVP Product, Operations and Marketing and John Roese, the new SVP and CTO of EMC (6 months on the job). Jeremy talked about an IDC insight that there’s a new world emerging so-called 3rd platform applications based on mobile and consumer grade technology  with literally billions of users, millions of apps built on mobile-cloud-bigdata-social infrastructure which complements the 2nd platform built on lan/wan, client server frameworks.

For an example of this environment Jeremy mentioned that AT&T provisions 12PB of storage a month.

What’s needed for this new platform is a new type of storage built for the 3rd platform but taking advantage of current enterprise storage characteristics.  This is ViPR (more on that later)

John comes by way of Huawei, Nortel and myriad others and offers a broad insight to the way forward for EMC. It looks like a bright future ahead if they can do half of what John has outlined.

John talked about the intersections between the carrier market (or services), enterprise IT and consumer market.  There is convergence between these regions and at each of these intersections new technology is going to answer many of the problems which exist. For instance in the carrier space:

  • The amount of information they gather is frightening they know everything about you. Pivotal will be the key here because its good at 1) ability to correlate information across different information sources. Most carriers have a whole bunch of disparate information stores; and 2) It’s not just focused on Big Data as a non-realtime problem but also provides realtime analytics as well.
  • Capital costs are going down but $/bits are going way down.  VMware & Software defined data center is the right way to drive down costs.  Today servers are ~50% virtualized but networking is not virtualized at all.
  • Customers are dissatisfied with service providers (carriers).  Again Pivotal is key here. One carrier customer was focused on customer churn and tried to figure out how to minimize this. They used  Gemfire’ high speed infrastructure that could watchc all transactions on cell tower infrastructure pick out dropped calls, send it to Greenplum and correlate this with the customer attributes (good or bad), and within 100msec supply an interaction with the customer in to apologize and offer some services to make it better.
  • Internet is the new wild west –use at your own risk,  spoofing websites, respond to email could be anyone, chaos to security. RSA can become the trusted internet provider by looking at the internet holistically, combining information from many customers, aggregating and sharing these interactions to deterimine the trust of every transaction. Trust is becoming a new big data problem.
  • Hybrid and public cloud is their biggest opportunity but they don’t know how to attack it. VMware and SDDC will evolve to provide orchestrated movement from private to public and closed to open.

The thinking seems pretty straightforward given what they are trying to accomplish and the framework he applied to EMC’s strategy going forward made a lot of sense.

20130506-172955.jpgBrian Gallagher did a keynote on enterprise storage new functions and features which covered VMAX, VPLEX, RecoverPoint, and XtremIO/SF/SW. Mentioned RecoverPoint virtual appliance and sort of a statement of direction on being able to move application functionality directly on VMAX. He kind of demoed this with VPLEX running on VMAX.

He also talked about FAST speed of reaction versus the competition, mentioned that FAST provides information about the storage tiering to up to 4 different VMAX arrays. Showed a comparison of VMAX 10K against another prime competitor that looked downright embarrassing.  And talked about VMAX cloud edition.

20130506-173022.jpgAfter that 1 on 1 meetings all under strict NDA. But then the big Keynote with Jeremy again and David Goulden President and COO on ViPR. They have implemented software defined storage (SDS).  Last week I did a post on SDS trying to layout some of the problems and promises of SDS (please see The promise of SDS post).

But what I missed was the data path transformation that ViPR can do to provide object and HDFS access to traditional and commodity storage systems.  ViPR starts out primarily in the control layer providing automated provisioning, self management, across heterogeneous storage pools. With ViPR one can define virtual storage arrays and then configure virtual storage pools across those arrays regardless of the physical infrastructure underneath them.

More on ViPR in a separate post but suffice it to say EMC has been working on this for awhile now. But how it’s positioned with VPLEX and the other storage virtualization capabilities in VMAX and other products is another matter. But it seems they are carving out a space for ViPR between and above the current storage solutions.

End of day one is in the Expo and then cocktail parties… stay tuned for day 2.


Mobile is exploding

We have talked about this before but more facts have come to light regarding the explosion of mobile data traffic, signaling a substantive change in how the world accesses information. In the Rise of mobile and the death of the rest the focus was on the business risks and opportunities coming from the rise of mobile computing.

Just yesterday, in an article in MIT’s Technology Review there was even more evidence of the rise of mobile.  The article, titled Four Charts That Illustrate the Transformation of Personal Computing was a summation of a Mary Meeker of KPCB presentation on the state of the internet (slide share embedded above).

The MIT article had one chart (see slide 15 above in Mary’s deck) that showed mobile internet traffic in November 2012 was 13% of all internet traffic which grew from a base level of ~1% in Dec 2009.  So in roughly 3 years, mobile traffic is consuming over 13X more internet bandwidth than any other type of device.

Just in case you needed more convincing, in another article in MIT Technical Review (this one on spectrum sharing) Cisco was quoted as saying that mobile traffic would grow 18X by 2016.

If mobile’s winning, who’s losing?

That has got to be desktop computing.  In fact, another chart (slide 16 in Mary’s deck) showed a comparison of India’s internet traffic tracking desktop vs mobile devices, from December 2008 to November 2012. In this chart India’s mobile internet exceeded desktop traffic sometime the middle of this year.

But I think the one chart that tells this better (see one slide 25) shows that smartphones and tablets shipments exceeded desktops and laptops in 2010.  The other interesting thing is that one can also see the gradual decline in desktops and laptop shipments since then.

Where’s the revenue streams?

The funny thing about Mary’s presentation is the fact that she was tracking mobile app and mobile advertising (see slide 17) as a rising revenue opportunity, expected to reach $19B in 2012.  In my post on the rise of Mobile, I assumed that mobile advertising would not be a successful model for mobile revenue streams – I was wrong.

Mary’s presentation also showed some of the impact of mobile on other markets and foretells the future impacts mobile will have. One telling example for this is standalone camera sales vs mobile camera shipments (see slide 32) which crossed over in 2008 where standalone camera sales peaked at~150M units. The same thing happened with standalone personal navigation devices (PND)  (see slide 34) that peaked 13M units in 2009 but where Waze unit (mobile navigation aid) exceeded PND unit shipments in Q1 2012.

The remainder of the presentation (at least what I read) seemed to define a new life-style option she called Asset-Light which was all about shedding physical assets like wallets, paperback books, TV and other screens, fixed LAN connectivity and moving to a completely mobile world where everything you need is on your tablet with access to the internet via WiFi or LTE.

Mobile is here, better get ready and figure out how to do business with it or consider this a great time to curtail your growth prospects.