The IaaS and PaaS cloud models allow architects to decouple components of an application or enterprise system into the lowest functional components and design for failure how these pieces can be utilized as “independent black boxes” to form an application. This allows for provisioning elasticity and resiliency of individual components and their states in the inevitable event of hardware or software failure.
One of the least understood impacts of this approach is that the message queues used by components can become the most important elements in assuring availability, scalability and ultimate reliability. In essence the messaging infrastructure components become the most critical parts of an applications infrastructure designed to exploit elasticity. If you envision these Enterprise Apps as complex organisms, then the message queues and their reliability become mission critical organs of the living, agile enterprise architecture. Components such as controller apps, databases and such should be isolated allowing buffering of request along with replies making the network of components more durable and state independent facilitating failover and scalability.
The influx of companies trying to exploit “Big Data” as a new revenue source has provided a number of workforce challenges for senior managers. Do they hire very smart math folk to devise new algorithms and create a “secret sauce” for their products? Do they develop or acquire superiors hardware that leverages new Si technology to better process big data? Do they form teams that have practical business experience to ferret out which real problems exist in the marketplace and what approach to analytics will be truly appreciated by customers’ end users?
Well the answer is … a little of each! The most important thing many companies are missing today is that they focus on the technology and technologist in their hiring decisions but not the business logic experience. There is great value in having teams of technology folk embedded with thought leadership coming from experience. Bright, eager, smart people, with minimal experience know theory and math but don’t know human behavior in business. They also don’t have the understanding of the technology assimilation hurdles that form huge barriers to rapid adoption and market share growth. The targeted customer base will often need help understanding:
How much data do we have?
What is actionable information contained in the big data fog?
How much information do we need to make decisions?
What changes in data are significant and require action?
What is a practical “on ramp” to use big data technology?
The bottom line is an integrated team of smart technologist stewarded through development with experienced thought leadership will result in the “BIG THEORY” required to make big data solutions palatable and easily digested by the human organism we call an enterprise. Where in reality, meaningful mobile visualization transforms BIG DATA into actionable information.
As enterprises come to grips with Cloud Computing demands (both internal and external) the IT groups will soon realize that the Hybrid model is the “best fit” for the new Enterprise IT organization. This will also force a closer alignment with various business units and provoke a rethink of the costing models for IT. can IT really stay a coast center given the inevitable variable demand curve of Cloud Services? Enterprise IT shops will consider various vendors (E.G., Azure, HP, VMware, Amazon & others) in light of the matrix created by matching customers service type needs to flexibility of leveraging a vendors Cloud Service offerings to suit the enterprise’s complex business needs. the ease of entrance and exit will be the driving forces behind vendor selection not just cost but ease of achieving true operational excellence.
Finance, Corporate Strategy, Biz Units and IT will collaborate to determine which “flavor” of Cloud Services are needed. For example the SaaS, IaaS or PaaS models may all be needed in the view of the business objectives. The decision of what kind of service offerings to implement will drive IT’s customers to do a functional decomposition of existing applications and distil what services are used today. This will lead to an “applicability analysis” of which type of Cloud implementation makes good business sense. Some may choose from Cloud Platform as a Service, Cloud Infrastructure as a Service, Cloud as a Software Service model. These may also include convent “off ramp & on ramp” strategies to allow customers to switch as circumstances dictate. An example of the choices is illustrated below:
Whatever Business Situation Determines That’s the Right Cloud Service Choice
As we look at today’s complex product, business and end-user requirements, some key ideas must be addressed to achieve profit margin goals. Almost all electronic products today utilize software, hardware and multiple suppliers/vendors to complete product functionality. The chart below is meant to trigger thinking about key items that must be included in the today’s electronic product engineering process.
These must come in to play while engineering products today.
When we look at the history of the PC industry, we see that while Moore’s Law is fantastic, it is always outpaced by consumer demand. Market expanding software solutions can be developed faster than hardware solutions to develop but are frequently performance constrained by the limits of running on general purpose processors. Eventually IHVs see a large enough market and have time for development of custom silicon to parallelize the process. This lag time between when the problem is first noticed and when it’s solved in silicon can be referred to as the “Wilson Gap” aphras coined by some Microsoft employees who worked with me and quoted my assessment as “Information consumer appetite/demand will always outpace CPU capability” which I stated in a meeting regarding complex computational transforms.
By doing a simple analysis of this “Wilson Gap” over a series of technologies we can see some very interesting patterns:
Wilson Gap analysis
*Note: This illustration is based on 2011 estimates
The vertical axis represents the number of years a particular technology was on the market in software-only form before it was introduced in silicon as an ASIC (Application Specific Integrated Circuits). Based on this data I would like to postulate that companies like Microsoft & Google have direct bearing on these figures, and that in many cases they can significantly reduce the Wilson Gap. But first, let’s review the situation a little further.
How the SW Industry Fights the Wilson Gap
While the flexibility general purpose CPU offers imaginative engineers the ultimate design surface, it likewise has the inherent limitation that code must be reduced to a lowest common denominator, that being the CPU instruction set. Time and again, this limitation has caused a Wilson Gap in what consumers want and what the PC platform is able to inherently deliver.
For Many of Today’s Needs Moore’s Law is too Slow
As the previous graph illustrates, the Wilson Gap was a limiting factor in the potential market for specific technologies, when the CPU was not fast enough for the consumer demand of floating point operations. Likewise, at various times throughout PC history, the CPU has not kept up with demand for:
Digital Signal Processing (DSP)
3D Graphics
SSL Processing (encompassing 3DES, RSA, AES)
MPEGx Encoding/Decoding
Windows Media Encoding/Decoding
TCP/IP offloading
XML Parsing and Canonicalization
ASICs help reduce the Wilson Gap
When Moore’s Law is too slow we traditionally rely on ASICs to fill the Wilson Gap. In all of the examples above (Math Coprocessor, DSP, 3D, 3DES, RSA, MPG, etc…) we now have fairly low-cost ASICs that can solve the performance issue. Total time to solution and time to money are far too long for current industry economic conditions. These (ASIC) processors will typically accelerate a task, off-load a task or perform some combination of the two. But for the remainder of this paper we’ll use the term “accelerate” to include acceleration that encompasses CPU off-loading.
The Downside to ASIC Solutions
Unfortunately ASICs are inherently slow to market and are a very risky business proposition. For example, the typical ASIC takes 8 to 12 months to design, engineer and manufacture. Thus their target technologies must be under extremely high market demand before companies will make the bet and begin the technology development and manufacturing process. As a result, ASICs will always be well behind the curve of information consumer requirements served by cutting edge software.
Another difficulty faced in this market is that ASIC or Silicon Gate development is very complex, requiring knowledge of VHDL or Verilog. The efficient engineering of silicon gate-oriented solutions requires precision in defining the problem space and architecting the hardware solution. Both of these precise processes take a long time.
FPGAs further reduce the Wilson Gap
A newer approach to reducing the Wilson Gap that is gaining popularity is the use of Field Programmable Gate Arrays (or FPGAs). FPGAs provide an interim solution between ASICs and software running on a general purpose CPU. They allow developers to realign the silicon gates on a chip and achieve performance benefits on par with ASICs, while at the same time allowing the chip to be reconfigured with updated code or a completely different algorithm. Modern development tools are also coming on line that reduce the complexity of programming these chips by adding parallel extensions to the C language, and then compiling C code directly to Gate patterns. One of the most popular examples of this is Handel-C (out of Cambridge).
The Downside to FPGA Solutions
Typically FPGAs are 50% to 70% of the speed of an identical ASIC solution. However, FPGAs are more typically geared to parallelize algorithms and are configurable so as to received updates, and leverage a shorter development cycle (http://www.xilinx.com/products/virtex/asic/methodology.htm). These factors combine to extend the lifespan of a given FPGA-based solution further than an ASIC solution.
A Repeating Pattern
Looking at the market for hardware accelerators over the past 20 years we see a repeating pattern of:
First implemented on the general purpose CPU
Migrated to ASIC/DSP once the market is proven
Next the technology typically takes one of two paths:
The ASIC takes on a life of its own and continues to flourish (such as 3D graphics) outside of the CPU (or embedded back down on the standard motherboard)
The ASIC becomes obsolete as Moore’s Law brings the general purpose CPU up to par with the accelerator by the new including instructions required.
Now let’s examine two well known examples in the Windows space where the Wilson Gap has been clearly identified and hardware vendors are in the development cycle of building ASIC solutions to accelerate our bottlenecks.
Current Wilson Gaps
Our first example is in Windows Media 9 Decoding; ASIC hardware is on its way thanks to companies such as ATI, NVIDIA and others. This will allow the playback of HD-resolution content such as the new Terminator 2 WM9 DVD on slower performance systems. Another example here is in TCP Offload Engines (TOE); which have recently arrived on the scene. Due to the extensibility of both the Windows’ Media and Networking stacks, both of these technologies are fairly straightforward to implement.
Upcoming Wilson Gaps – Our Challenge
However, moving forward the industry faces other technologies which don’t have extensibility points for offloading or acceleration. This lack of extensibility has lead to duplication of effort across various product teams, but not duplication in a competitive sense (which is usually good), but more of a symbiotic duplication of effort, increasing the cost of maintenance and security.
An example of leveraging Cloud Services is to deploy an application that services the healthcare industry by ultilizing the Infrastructure as a Service(IaaS) model E.G., Azure:
To deploy a Cloud-based Azure Platform meeting HIPAA regulations, all application code segments must be designed using a web-services model where database elements and application code running in the cloud publish secure streams
Windows Azure allows an organization to create virtual machines (VMs) that run in Microsoft datacenters. Suppose the organization wants to use those VMs to run enterprise applications or other software that will be used by customers. We can create a SharePoint farm in the cloud, for example, or run HIIPA data management enterprise HITECH applications. To make life as easy as possible for our users, these applications would be accessible just as if they were running in an cost intensive local datacenter.
The Enterprise offering the Cloud Services must follow these five rules in order to stay comliant with HIIPA:
Internet “Things” will change the way people interact with information
SRI Things Roadmap
While consumers fall in love with “personal clouds“, Enterprises will begin deployment ofhybrid clouds that combine public and enterprise only secure cloud infrastructures,
Using clear targets in various sectors, startegic vision can be achieved
If an enterprise needs to establish a strategic vision that maps out a clear path to an end state vision, then specific action items can be set around well defined targets in:
Strategy Targets that help expand the footprint of a technology or products
Process Targets the sheppard teams to accomplish goals and deciplined cycles of activity
People Targets that help increase productivity and creativity
Business Targets that set fiscal milestones and performance meterics
Ecosystem Targets that help stimulate the health and growth of ecosystem partners and fellow travelers.
Below is an example of process targets that help:
sing Strategic Process to sheppard an enterprise
Strategic Technology Planning Process
Review existing technical plans and strategic direction
Develop a Technology Mission Statement
Analyze Current raw Data
Establish Goals and Objectives
Develop and Implement Project Plans and Timelines (Roadmaps)
Disseminate, Monitor, Evaluate, Renovate the Technology Plan.
My Conclusion on Si Architecture Trends and thier ecosystem impact
Today’s Si companies must track the key trends in Si technology development, assembly test, Nanotechnology, Cooling, Emerging Research, Virtualization, acceleration and Si Complex Architectures to help drive their product teams in close collaboration with other Si vendors to keep the enterprise in a thought leadership position contemporary with the Silicon Industry along with consumer demands.
This blog is intended to document key technology trends and issues I feel will have a major impact betwen now and 2035. The following areas will be covered:
