#eavoices

RACI for Enterprise Architecture

by

Companies usually have installed an executive hierarchy. This hierarchy is one that has been evolved and tested over a long period of time. We have reasonably clear ideas about what it should establish. People have one boss, who more or less decides about their roles, assesses their performance, and decides what to do when the […]

Defensive denial

by

… often follows what Freud called “kettle logic“.

“The problem doesn’t exist, and anyway it isn’t a problem for us, and anyway we’re already dealing with it.”

Imagine we ask a manager whether her organization experiences any of the Symptoms of Organizational Stupidity. Suppose she denies it, or says it doesn’t matter. Guess what – defensive denial is one of the symptoms on the list.

In 2008, Blockbuster CEO Jim Keyes expressed some bewilderment at his competitor’s success.

“I’ve been frankly confused by this fascination that everybody has with Netflix. … Netflix doesn’t really have or do anything that we can’t or don’t already do ourselves.” 

Denial is a key phase in the hype curve for new ideas (especially but not exclusively technological ones). A concept is rejected as meaningless, dangerous and/or unnecessary, while simultaneously being bundled together with earlier concepts.

“That concept doesn’t make sense, and even if it did it wouldn’t be technologically feasible, and anyway we already have a perfectly good word for it and lots of people are already doing it so we don’t need a new word.”

Brian Klapper, When Corporations Cannot Adapt (aka Fear the Kid in the Black t-shirt) (January 2013)

Related Posts: The Dynamics of Hype (Feb 2013)

Microservices and the Internet of Things – First impressions

by
I must say I was sceptical when I first heard the term “microservices”. It sounded like yet another wash-rinse-repeat cycle of earlier incarnations of SOA. It appears I was wrong – this architectural pattern has some  interesting characteristics that, in my opinion, offer some real potential for event-driven, edge-processing systems (that are prevalent in the Internet of Things).
After watching Fred George’s video, I realised what he described was an event-driven, agent-based, systems’ model, rather than how many of us see SOA implementations today (often way-off the original notion of a SOA). At a conceptual level, the pattern describes a ‘Complex Adaptive’ system.  Essential principles of the architecture, however, appear teasingly elegant and simple. Few of these design principles are unique to microservices, but in combination, they make a compelling story:
Publish anything of interest – don’t wait to be asked, if your microservice thinks it has some information that might be of use to the microservices ecosystem, then publish-and-be-damned.



Amplify success & attenuate failure – microservices that publish useful information thrive, while those that left unsubscribed, wither-on-the-vine. Information subscribers determine value, and value adjusts over time/changing circumstances.



Adaptive ecosystem – versions of microservices are encouraged –may-the-best-service-win mentality introduces variety which leads to evolution.



Asynchronous & encapsulated – everything is as asynchronous as possible – microservices manage their own data independently and then share it in event messages over an asynchronous publish-subscribe bus.



Think events not entities – no grand BDUF data model, just a cloud of ever-changing event messages – more like Twitter than a DBMS. Events have a “use-by-date” that indicates the freshness of data.



Events are immutable – time-series snapshots, no updates allowed.


Designed for failure – microservices must expect problems and tell the world when they encounter one and send out “I’m alive” heart-beats.



Self-organizing & self-monitoring – a self-organizing System-of-systems’ that needs no orchestration. Health monitoring and other administration features are established through a class of microservices.



Disposable Code – microservices are very, very small (typically under 1000 lines of code). They can be developed in any language.



Ultra-rapid deployment – new microservices can be written and deployed with hours with a zero-test SDLC.

It struck me that many of these design principles could apply, in part, to a 2020 Smart Grid architecture I’m working on, and to the much boarder ‘Internet of Things’ecosystem.

The microservices pattern does seem to lend itself to the notion of highly autonomous, location-independent s/w agents that could reside at the centre, mid-point or edge of an environment. I can imagine that the fundamental simplicity of the model would help, rather than hinder, data privacy and protection by being able to include high-level system contexts, policies and protocols (e.g. encryption and redaction) applied to the event-streams. This pattern, of course, won’t be the ‘right-fit’ for all situations, but it does seem to offer interesting opportunities in:

  • Agility – very small disposable services are deployable within hours
  • Resilience – withstands service failures and supports service evolution
  • Robustness – it’s hard to break due to: simplicity, in-built failure handling and lack of centralized orchestration

It may be that the microservices pattern can only be applied to operational decision-support and behaviour profiling situations. But if that’s the case, I still see great potential in a world where many trillions of sensor-generated events will be published, consumed, filtered, aggregated, and correlated. I’m no longer a developer, but as an architect, I’m always on the look-out for patterns that could: either apply to future vendors’ products and services, or could act as a guide for in-house software development practice.

As always, I’d be keen to hear your views, examples and opinions about microservices and their potential application to the IoT. Have you come across examples of microservices pattern in an IoT context – deployed or in the labs?

I whole-heartily recommend setting aside an hour to watch the video of Fred George’s presentation on microservices:


131108 1110 Dune Fred George Recording on 2013-11-08 1106-Vimeo from Øredev Conference on Vimeo.

Post-post:
  • Another great post about microservices  – including downsides.
  • More here including “The 8 fallacies of distributed computing”.
Duke Energy are doing some interesting things in the Edge Processing space.

Here’s a video on microservices in the conext of IoT  (worth ignoring the references to Cloud/Azure):

http://www.microsoftvirtualacademy.com/training-courses/exploring-microservices-in-docker-and-microsoft-azure

I’d like to talk to anyone who’s impelmenting/ thinking about a Staged Event Driven Architecture using microservices for Edge Processing.

Phil Wills on experience of deploying microservices at The Gaurdian

Microservices and the Internet of Things – First impressions

by
I must say I was sceptical when I first heard the term “microservices”. It sounded like yet another wash-rinse-repeat cycle of earlier incarnations of SOA. It appears I was wrong – this architectural pattern has some  interesting characteristics that, in my opinion, offer some real potential for event-driven, edge-processing systems (that are prevalent in the Internet of Things).
After watching Fred George’s video, I realised what he described was an event-driven, agent-based, systems’ model, rather than how many of us see SOA implementations today (often way-off the original notion of a SOA). At a conceptual level, the pattern describes a ‘Complex Adaptive’ system.  Essential principles of the architecture, however, appear teasingly elegant and simple. Few of these design principles are unique to microservices, but in combination, they make a compelling story:
Publish anything of interest – don’t wait to be asked, if your microservice thinks it has some information that might be of use to the microservices ecosystem, then publish-and-be-damned.



Amplify success & attenuate failure – microservices that publish useful information thrive, while those that left unsubscribed, wither-on-the-vine. Information subscribers determine value, and value adjusts over time/changing circumstances.



Adaptive ecosystem – versions of microservices are encouraged –may-the-best-service-win mentality introduces variety which leads to evolution.



Asynchronous & encapsulated – everything is as asynchronous as possible – microservices manage their own data independently and then share it in event messages over an asynchronous publish-subscribe bus.



Think events not entities – no grand BDUF data model, just a cloud of ever-changing event messages – more like Twitter than a DBMS. Events have a “use-by-date” that indicates the freshness of data.



Events are immutable – time-series snapshots, no updates allowed.


Designed for failure – microservices must expect problems and tell the world when they encounter one and send out “I’m alive” heart-beats.



Self-organizing & self-monitoring – a self-organizing System-of-systems’ that needs no orchestration. Health monitoring and other administration features are established through a class of microservices.



Disposable Code – microservices are very, very small (typically under 1000 lines of code). They can be developed in any language.



Ultra-rapid deployment – new microservices can be written and deployed with hours with a zero-test SDLC.

It struck me that many of these design principles could apply, in part, to a 2020 Smart Grid architecture I’m working on, and to the much boarder ‘Internet of Things’ecosystem.

The microservices pattern does seem to lend itself to the notion of highly autonomous, location-independent s/w agents that could reside at the centre, mid-point or edge of an environment. I can imagine that the fundamental simplicity of the model would help, rather than hinder, data privacy and protection by being able to include high-level system contexts, policies and protocols (e.g. encryption and redaction) applied to the event-streams. This pattern, of course, won’t be the ‘right-fit’ for all situations, but it does seem to offer interesting opportunities in:

  • Agility – very small disposable services are deployable within hours
  • Resilience – withstands service failures and supports service evolution
  • Robustness – it’s hard to break due to: simplicity, in-built failure handling and lack of centralized orchestration

It may be that the microservices pattern can only be applied to operational decision-support and behaviour profiling situations. But if that’s the case, I still see great potential in a world where many trillions of sensor-generated events will be published, consumed, filtered, aggregated, and correlated. I’m no longer a developer, but as an architect, I’m always on the look-out for patterns that could: either apply to future vendors’ products and services, or could act as a guide for in-house software development practice.

As always, I’d be keen to hear your views, examples and opinions about microservices and their potential application to the IoT. Have you come across examples of microservices pattern in an IoT context – deployed or in the labs?

I whole-heartily recommend setting aside an hour to watch the video of Fred George’s presentation on microservices:


131108 1110 Dune Fred George Recording on 2013-11-08 1106-Vimeo from Øredev Conference on Vimeo.

Post-post:
  • Another great post about microservices  – including downsides.
  • More here including “The 8 fallacies of distributed computing”.
Duke Energy are doing some interesting things in the Edge Processing space.

Here’s a video on microservices in the conext of IoT  (worth ignoring the references to Cloud/Azure):

http://www.microsoftvirtualacademy.com/training-courses/exploring-microservices-in-docker-and-microsoft-azure

I’d like to talk to anyone who’s impelmenting/ thinking about a Staged Event Driven Architecture using microservices for Edge Processing.

Phil Wills on experience of deploying microservices at The Gaurdian

Cynicism is Not a Skill

by

For six weeks and with about $10,000 I had toiled to create an additional bathroom in my previous home just in time to host some relatives during the holidays. I only had time to throw one of our better bath towels over the curtain rod as this element…

Cynicism is Not a Skill

by

For six weeks and with about $10,000 I had toiled to create an additional bathroom in my previous home just in time to host some relatives during the holidays. I only had time to throw one of our better bath towels over the curtain rod as this ele…

Mapping Agile Architecture

by

Jason Bloomberg recently published a mind map for Agile Architecture. It’s a nice map that sketches top level thinking and I welcome that. It prompted me to do a drill down.

Mind maps are useful in that they are, by definition free form and intended to support brain storming. The downside is obvious – they are generally inconsistent and cause modelers’ intense frustration! Caveat emptor over, I fully agree with Jason that we need a dual interpretation of Agile  – that is Agile practices and Agile Architecture, and I have written about this on many occasions. Also that the entire motivation is about business agility. On this last point my mind map is clearly a little more technical than Jason’s, and on reflection I think that is because it’s essential to converge the business and technology concerns.

For example, the map suggests a strong capability centric approach to interpret the business morphology. However this is insufficient; the technology must also establish appropriate levels of implementation independence that will facilitate the pluggability of business capabilities. Similarly you might think that considerations regarding the platform and delivery technology (such as MDA/MDD) are irrelevant to business concerns. However the platform and platform delivery technology are potentially massive drivers of rapid iteration and ongoing change, because they encapsulate common application level infrastructure and common services, so understanding the “business” standardization and localization model is crucial to delivering agility through this structure.

References:
Related posts: 

Scaling Agile means Scalable Architecture

by

With the growing popularity of Agile, mainly in applying Scrum for IT development, issues need to be tackled relating to scaling up the development effort. Scrum came to birth in small teams that had a lot of mandate, typically 3 to 10 person teams. Now we see Scrum used for major efforts, involving hundreds of […]

How Do You Measure Up?

by

One of my life-long idols, Muhammad Ali is quoted as saying, “The man who views the world at 50 the same as he did at 20 has wasted 30 years of his life.” Can you point to a specific situation where you have changed your mind? On the topic of a…