What do GitHUB and GrabCAD Have in Common?

During our last board meeting one of our directors mentioned a start-up he thought was interesting: GrabCAD. Awhile ago I had read about this company on Techcrunch, but since the company was in the CAD/CAM space I filed a note in a brain cell memory register “interesting company, but not something I need to follow closely.”

But a different thought took hold; Hmmm… GrabCAD is to the CAD/CAM professional the same way GitHUB is to the software professional. We’re witnessing the rise of start-ups that cater to “niche” audiences who create a certain kind of content as their prime means of professional affiliation. Don’t take offense to the term niche audience applied to software or CAD professionals. It’s just a way to say “not a mass audience” that is served by a general purpose content creation site like Tumblr, WordPress.com, etc.

GrabCAD targets the CAD/CAM professional with a CAD-specific sharing space augmented with a thin “social network layer.” Create a drawing, upload to GrabCAD, post a link “hey look what I created” and share, trade, and sell your work product. It’s not a place to generate generalized content (like Google Docs, ZoHo, or Office 365), but rather a sharing space for affiliated professionals that want to showcase “their wares.”

GitHUB is a content sharing system that targets the software professional. In this case, “content equals source code.” It’s really “social source code management” with a bunch of other goodies like wikis and pasties mixed in. Source code management has been around forever, but GitHUB makes it really easy to share and integrate code from various projects. Developers don’t actually write their code in GitHUB, they do that in their own developer environments, just like CAD professionals don’t use GrabCAD to create drawings.

In the software world, it is now common for developers to tout their GitHUB account URL as a living resume. You can imagine the CAD/CAM professional one day sharing links to their GrabCAD creativity just like software developers share their GitHUB awesomeness.

Catering to a large niche audience with a custom experience is a successful end-run around mass appeal social networks like Facebook. The core required features, such as file upload, link sharing, and comment curation exist in many platforms, from WordPress to Drupal, to Facebook. But a generic user experience will not suffice. GrabCAD speaks the language of the CAD industry. GitHUB does the same for the software industry.

There are other examples of this trend, although none as focused as GrabCAD or GitHUB:

  • Prezi and Slideshare for presentations, although not specifically targeted toward a specific profession.
  • Scribd for documents. But not targeted to a specific industry.
  • Disqus for comments? Would it be a stretch to cite Disqus for the professional commenter? Probably, but an interesting idea.
  • Basecamp for project managers.
  • Sortfolio for web designers.

I can imagine other industries ripe for this niche audience approach: legal (specialized documents), chemical (formulas), teachers (lesson plans), music (lyrics). Easy and clear content owner attribution will need to be resolved for some of these ideas to be successful.

I’m excited to see the next GrabCAD come to life. If you know any vertically aligned professions where content creation is the core work-product, scratch your entrepreneurial itch and create a niche audience user experience now.

 

New Cloud Rules: Replace Instead of Fix

Here’s an all too common scenario from the “cloud chronicles.” A virtual machine that has been operating just fine for days, and has 50 other identical twins with the same configuration, starts to exhibit problems. Slow virtual disk performance. Network brown-outs. Disconnecting and reconnecting within it’s functional cluster. Monitoring systems alert on degrading performance, and the knee-jerk response is to jump on the box (nee VM) and start to troubleshoot the issue. The problem is, spending any time troubleshooting an anomaly in the “cloud” is the wrong reaction. In the cloud, the first response, when a node starts to exhibit erratic behavior, should be to replace, not fix.

Replacing, instead of fixing, goes against the ingrained habits of over two decades of entrenched IT best practices. In the pre-cloud world, when real hardware was the base, we had to “fix IT” because replacing was too expensive and not practical. There was not an endless pile of spares lying about for a “replace IT” mindset.

But in the cloud, with, in theory, nearly infinite CPU, the remediation to an errant node should be to immediately replace, and move on.

Why Is This?

Because there are too many causes beyond our control at the OS level in a cloud environment. Think of the cloud like living in a high-rise building. Each unit in the building, just like each cloud customer, can have whatever interior they want, but there are also massive shared resources in the building. So while our interior may be a candidate for the next architectural digest cover, our neighbor could “kill our chill” with a too-loud stereo boom box. The cloud suffers from the noisy neighbor problem just like our theoretical high-rise. But in the cloud, we can choose to move and jump back into the random lottery for a new unit. We can’t change the building, but we can change the location within the building.

Of coure, you need the right cloud-centric architecture to be able to simply “replace IT” instead of “fix IT.” Having cloud-dexterity is critical to operating a successful cloud deployment.

The cloud requires us to “un-learn” the best practices of the past and embrace a new way of thinking about “break fix.” While replacing instead of fixing may seem wasteful, it’s really not. The time spent troubleshooting the random problem will not yield significant insights, and could be better spent focusing on more value-add projects. Usually after extensive diagnosis, the only recourse is to replace the node, since the original problem was an outlier.

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Boston’s Emerging Cloud “Swagger”

This week is a “cloud-themed” double treat for me. I attended both Cloud Connect and the San Francisco Cloud Mafia meet-up. Cloud Connect has become an annual Sonian tradition. Sonian has been wrangling public cloud infrastructures for five years, and Cloud Connect is a great opportunity to “connect” with other cloud users and technology providers.

Ever since I heard about the first San Francisco Cloud Mafia event I have wanted to attend. The challenge was justifying a West Coast trip to coincide with the meet-up date. My fortunate luck that both events occurred the same week and within close physical proximity. Over 100 people attended Cloud Mafia last night to hear talks from AppFog, Loggly and New Relic. I was struck by the “electricity” in the air around this event. And the topic was more “nuts and bolts” about cloud management compared to the topics of the previous business-themed Cloud Mafia meet-ups.

Boston has many great “cloud” technology companies and enthusiastic individuals supporting the cause, but not as many compared to the activity in San Francisco and Silicon Valley. Sonian sponsors Monday’s in the Cloud for Boston-area cloud aficionados looking for their cloud fix. In addition, there are numerous “big data” and entrepreneurial events sponsored by MassTLC,  BostonInno, CloudInno and others at venues such as Microsoft NERD, Royal Sonesta Cambridge and Foley Entrepreneurial Center in Waltham, MA.

I’ve detected an emerging “Boston Cloud Swagger” throughout 2011 and increasing in 2012. More companies innovating in the cloud, solving interesting problems, and contributing to the home town technology eco-system. Even in my own presentations, meetings and blogging about Sonian’s cloud accomplishments, my articulation is that of “seasoned cloud veteran,” which reflects Sonian’s commanding lead in the cloud. It’s a swagger well earned through our cloud “trials and tribulations.”

There have been many comparisons between Boston and Silicon Valley start-up scenes. The basic sentiment of the past was there seemed to be more innovation occurring on the West Coast than here in the East. There are certainly more companies in the West, but Boston isn’t far behind with our own growing tech scene created by our universities and deep historical roots in innovation, especially for enterprise IT.

The West Coast does take the lead in number of gatherings where tech folks meet to share ideas. Boston feels more insular. The Valley’s weather provides more year-round opportunities and incentives to make time for meet-ups. Sometimes it’s hard to get motivated to head out, after a long day at the office, on a frigid February night to a tech meet-up. There has to be an anticipated reward for the effort.

I feel energized from last nights Cloud Mafia meet-up and will amplify that sentiment by contributing to Boston’s very own Cloud Swagger.

 

Cloud Success Requires Cost-aware Engineering

This is a true story from the “Cloud Cost Czar Chronicles.”

Our S3 “penny per one thousand” API costs started to rise rapidly in the second half of the cloud infrastructure billing period. We have seen this behavior before, and knew this could be attributed to increased usage, a new defect, or a design flaw that rears its head at a scaling tipping point. My job as “cost czar” is to raise the alarm and work with the team to figure out what was going wrong. At the observed rate of increase, the excess charges would push the monthly bill beyond the budget. One thing we have learned in the cloud, is that costs can rise quickly, but take awhile to go down, since the deceleration effect can be out of proportion to the acceleration if trying to manage expense in a single billing period.

When we started using Amazon Web Services S3 (a PaaS object store) back in 2007, we were acutely aware of the three pricing vectors in effect; storage consumed, price for API calls to store and list data and price for API calls to read and delete data. We’ve been using S3 heavily for five years and we tried to model the “all-in” costs as accurately as possible. But “guestimating” costs beyond the raw storage was stretch. PaaS services have an intrinsic “social engineering” element. If you color outside the lines the financial penalty can be significant. But if you master the pricing game, the rewards are equally as significant. So five years ago we thought as long as we point in the right general direction, “we’ll figure it out as we go along.” Some assumptions proved a positive surprise. Raw storage costs went down. Some surprises not so pleasant; continually wrangling the API usage fees, especially the transactions that cost a penny per thousand, proved to be a constant challenge. But I still like my options with S3 compared to buying storage from a hardware vendor and having to incur the administrative overhead. With S3 we can lower our costs by smarter engineering. With storage hardware, the only way to lower costs is to wrangle a better deal from an EMC sales person. As one of the original “cloud pioneers,” Sonian is not alone in this effort, and it’s been a real eye-opener for software designers to have to think about how their code consumes cloud resources (and expense) at scale. Because whether a penny per thousand or penny per ten thousand, when processing hundreds of millions of transactions a month, any miscalculation suddenly brings a dark cloud raining over your project.

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Apps as Entertainment and Other TV Paradigm Shifts

I was at dinner the other night with a business partner and the Sonian Customer Development team. The conversation turned to sharing our observations about how the commercial entertainment business struggles to embrace new technologies they view threatening to their “status quo.” Seems anytime a bunch of us technophiles congregate, this conversation theme comes up.

It’s because we see what’s technically possible and tantalizingly just around the corner. It’s because we are tired of being forced to pay for content we do not want. And it’s because the studios, and the providers (Comcast, TimeWarner, etc.) are fighting like hell to defer the inevitable.

It’s the beginning of a great battle brewing between old Hollywood & old infrastructures, versus Apple, Amazon, Netflix, Roku, Boxee, etc.

[Sidebar: I was part of the first act in this play: On the forefront 10 years ago with Tivo, ReplayTV, and the like as these pioneering companies took the first step to change the way  consumers watched the provider's content. Now Act 2 is a different set of vendors still fighting the same battle.]

At the dinner several of us have either already “cut the cord” or were about to do it. At the dinner several of us remarked that the iPad or Kindle Fire was the place where they consumed more content than ever before.

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Cost Transparency in the Cloud

Today Amazon Web Services lowered S3 “standard” pricing for storage volumes less than 450 terabytes. Standard service (STD) is the very reliable “eleven-nines” SLA. This is the original “gold standard” for cloud-based object store. S3 is a great example of Platform as a Service (PaaS) storage. This price decrease is interesting. In the past, instead of lowering the price of the standard service, Amazon creatred a new class of storage, Reduced Redundancy (RRS), with a different SLA and a different price. RRS is “four nines” of durability, and a lower price for less durable than “eleven nines.”

RRS was the recognition that cloud customers didn’t need “one size fits all” storage, but instead would benefit from different types of building blocks with lower price points, and varying service qualities. But in order to realize the lower price for RRS, AWS customers needed to write code or change behaviors. So today, AWS gave us a gift. The same reliable service we used yesterday, is five to ten percent less expensive today. With no work. We didn’t have to write one line of code.

This very public price reduction got me thinking about cloud cost transparency; in the cloud, all customers in the distribution chain know all the underlying costs. Our customers know how much we are paying for storage. So do our competitors. What this means is the cloud, different than the old co-located world, propels a new era of transparency and a healthy “checks and balance.”

Price transparency forces each value add layer in the cloud to amplify the innovation.

Amazon clearly found a better way to manage data and passed some or all of the savings to the customer. In turn, Sonian as a good cloud customer, will amplify with a positive change to our flagship archiving service.

The Joy of a Mobile Phone Free Agent

Hooray… starting this month I am a “mobile free agent!”. My two year lock in with Verizon Wireless is over. And boy are they trying harder than ever to keep me from straying.

So far Verizon Wireless has offered me a $50 cash gift card if I re-commit to another two years. (Wow… they must think I’m an easy mark!), or a new deluxe handset like an iPhone for $199, or a Droid at $149 or couple other offers that promptly went to the recycle bin.

Now I can re-think my mobile needs without incurring an early termination fee.

A quick calculation shows that over the past two years I spent $199 for an Android handset, and $125 a month for voice, data, text and tethering. That’s $3200 for two years of cell phone service. Pretty staggering when I look at that number with some perspective.

But what I *think* I get from Verizon, such as a supposedly superior network, makes me re-question my assumptions for why I should continue as a Verizon customer. The “strength of their network” was my primary appeal to VZW.

I’m in a good position to change carriers. Line number portability and Google Voice make the process really easy compared to just a few years ago. I can keep my number, and use Google Voice as a continuity bridge between the old and the new.

I investigated my options and found Ting. I like their whole approach to buying a mobile phone. Choose a handset, choose an al-a-cart voice/data/text plan, and no termination fees. But what gives me concern is that Ting is a MVNO for Sprint. An MVNO is basically a marketing and provisioning layer on-top of a second-tier wireless provider. I’m now confronted with whether I want to leave Verizon for another CDMA carrier, but one that may not have as an extensive network as Verizon.

This is an example of my inner-reasoning focusing on an extreme edge case, (what if I were driving in remote mountains where only VZW had towers?) instead of the majority situation condition (It’s very rare I am driving in remote mountains.) Why should the 99% suffer for the 1% fringe?

Ting will save me about $500 a year on a two-year contract compared to a comparable VZW suite of services. If I traded down from a smart-phone to a feature phone then the savings are even more dramatic; $1,300 a year.

I’m not ready to give up a smart-phone for a feature-phone, but I may be able to get over the fact that a second-tier mobile provider may be just fine.

I’ll post back here when I make my decision.

A 2007 Multi-Cloud Fantasy Becomes a 2012 Reality

Five years ago I wrote a business plan that described an archiving SaaS project built on cloud computing. In 2007 that was an uphill battle to convince prospective investors “the cloud was the future.” And at that time there was really only one cloud, from the e-commerce giant Amazon. Amazon Web Services really started the modern cloud movement. No existing IT provider (IBM, HP, Microsoft, Dell, etc.) would have had the gusto to upset their current business model with a “disruptively priced” cloud option. For the past four years those IT giants fought the cloud momentum until they had a credible cloud themselves. But for a lean start-up getting funded five years ago, it wasn’t a stretch to assume other clouds would appear to take on Amazon.

The graphic above was my crude way to visualize how a cloud-powered digital archive, anticipating someday living on multiple clouds, could in essence become a “cloud of clouds.” A lot of positive breakthroughs would need to occur to be able to successfully operate a single reference architecture software stack across more than one cloud. There was no terminology to describe this desire. We weren’t using terms like “Big Data” or “DevOps” nor many of the acronyms that today are common lingo in our modern cloud-enabled world. The business plan depicted a system designed to manage lots of data, and being an enterprise document archive, the data itself was large in size and numerous in quantity. We probably started one of the worlds first cloud-big-data projects.

In the beginning the multi-cloud goal was a fantasy dream, a placeholder for a future that seemed possible, but the actual crawl, walk, run steps not precisely defined because we didn’t yet know “what we didn’t know.”

So why in 2008 were we thinking about “multi-cloud?” The answer is we wanted to avoid single vendor lock-in and maintain a modicum of control over our infrastructure costs. The notion of an evolving multi-cloud strategy meant the ability to seek lowest cost of goods from multiple cloud vendors. In the pre-cloud IT world, when services were built on actual hardware, pricing flexibility was derived by negotiating better deals with hardware vendors. The customers didn’t know or care that their SaaS app might be powered by HP sever one day or a Dell 1U box the next. Those decisions were up to the discretion of the SaaS provider to get the best infrastructure value by shopping vendors. But in a single cloud, when there is only one choice, there’s no ability to negotiate between multiple vendors, unless you have multi-cloud dexterity.

Multi-cloud capable means the necessary infrastructure and abstraction layer is available to run a single common reference architecture on different clouds at the same time, with one master operator console. Multi-cloud is almost like, but not exactly, the concept of running a common program across IBM, DEC, Control Data mainframes. The clouds today somewhat resemble massive time-sharing mainframes of the previous decades.

Our early start five years ago, and all the hard lessons learned since, allows us to easily assume a commanding position in multi-cloud deployments. Engineering teams just now starting their “cloud journeys” will learn from us pioneers, but there is an old saying; “until you’ve walked a mile in my shoes, don’t claim to know anything otherwise.”

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The Problem with PaaS Pricing: Total Cost Uncertainty at Scale

 

Highlights of this post:

  • PaaS costs are difficult to predict at scale
  • IaaS costs are going down due to improved operational proficiency
  • Admin cost differences between IaaS and PaaS are negligible
  • PaaS should be less expensive to get better market traction

 

Here’s a handy decoder ring for all the acronyms in this post:

  • IaaS = Infrastructure as a Service. On-demand compute and storage typically available as an API call.
  • PaaS = Platform as a Service. On-demand turn-key web service that abstracts scaling, and reliability available as an API call.
  • AWS = Amazon Web Services
  • EC2 = AWS’s Elastic Compute web service
  • DIY = Do it Yourself
  • DevOps = Developer Operations… a new category for cloud systems management
  • GB-month = A pricing mechanism for cloud storage. Amount stored multiplied by hours stored multiplied by unit-cost per month during a billing period.

In the past I have written about the pros and cons facing cloud architects when choosing between an IaaS or PaaS solution for critical application infrastructure. Take a moment and read this post, Balancing Infrastructure as a Service (IaaS) versus Platform as a Service (PaaS), which focuses on the trade-offs between IaaS flexibility and PaaS’s vendor lock-in. There I briefly mention PaaS pricing challenges, so wanted to expand on that topic with a point of view on how current PaaS pricing schemes hinder adoption.

I’ll put my main theme right out here: Most PaaS solutions have a fundamental problem estimating operating costs at production scale.

There’s an implied “grand bargain” for cloud customers who expect an economic advantage for choosing a cloud PaaS service over a comparable cloud IaaS equivalent. From an anecdotal perspective that seems true. When using PaaS you expect lower people and development costs. PaaS is supposed to provide a price advantage because extensive operational efficiencies are supposed to lower costs. This is because massive physical and human expense are spread across many many customers. It’s a text book example of the “economies of scale.”

But wide-spread PaaS adoption is being hindered because cloud architects can’t wrap their minds around reliable cost estimation. Cost calculators, without real-world at scale metrics, give a false economic security.

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The Evolution of Purchasing Cloud Compute

In the beginning, as it were, just five years ago, purchasing cloud compute was simple because the rules were easy to understand and there were no choices. There was one cloud compute instance type that cost ten cents an hour. And a credit card was the only way to pay your monthly cloud compute bill.

Today there are a myriad of compute instances to choose from and multiple ways to pay for your cloud CPU time.

“Time…”

It’s the key word in the previous statement. The paradigm shift toward cloud computing away from the old dedicated co-lo world is bringing back the concept of purchasing compute “time.” Seasoned IT folks know there is nothing new to the concept of “buying” computer time. In the mainframe era (would it be hurtful to call it the IT Jurassic age?), computer time-sharing was the norm, and developers had to be mindful of how much computer time their programs consumed because mainframe’s were very expensive. In today’s dollars the equivalent of hundreds of dollars per hour.

Steve Wozniak, in his autobiography iWoz, tells a funny-turned-serious anecdote from his University of Colorado at Boulder freshmen year. He couldn’t return for his sophomore year because a program he executed on the University’s timeshare mainframe excessively consumed shared compute resources. The fees charged to his computer science department were astronomical for the era; more than $10,000.

Today’s cloud has the same gotchas: Watch out for excessive consumption. Once an hour has been consumed, there is no “return policy” to get your money back.

The cloud is pulling us back to that “time-sharing” mindset. We’re not buying 1U servers anymore. Instead, we’re buying virtual compute processing time based on haw many hours in a month a CPU runs, regardless of how much work was performed.

In the cloud, time is the unit of consumption and the month is the billing period.

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