Uncertain infrastructure - Dr. Mark Burgess Presentation at DevOps Days NYC 2013

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Mahesh Kumar

Mark Burgess explain Uncertain Infrastructure and ways around managing it in this DevOps Days presentation

Business Technology

Uncertain Infrastructures
What Science Tells Us About IT
Mark Burgess

P
H
Y
S
I
C
S

- DescriPing
- StuffH
- ThaYt
- HappenS
- ..ah
- ...
- .. ok whatever

Semantics and Dynamics
Desired-state and actual behaviour

Dev + Ops
What you intend + What actually happens

IT would be easy if systems were deterministic ...

Dev ?

Ops ?

… but they are only in approximate balance

LOGIC rains environmental complexity ...

… onto knife-edge sensitivity

Autonomous
“Continuous delivery requires continuous automation
infrastructure
... not power-assisted intervention”

There is no such thing as an immutable system
No intended change is not the same as no change at all

Failures are semantic
Processes are
dynamic

“The history of attempts to prevent cracks from spreading or evade
their consequences is almost the history of engineering.”
-- J.E. Gordon The New Science of Strong Materials

Control is ...

Harp not on that keyboard!
Orchestration?
Choreography?

Join the discussion …

Tw: @markburgess_osl

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Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946. And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…

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Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946. And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be had at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…

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It is not by accident that all these technologies appear to have come onto the scene at almost the same time. They are all driven, and or enabled, by the same hardware platforms based upon silicon with chip densities that now rival, or exceed, many biological lifeforms. Their ability to support increasingly complex software has seen AI and robotics become major industrial and medical tools. At the same time, Artificial Life is being applied in a more invisible manner, with Quantum Computing promising to change everything. So why are these technologies so important? In short; they allow us to tackle and understand the most difficult problems facing our species. And all of these are complex, non-linear, with emergent properties that defy our mathematical and computing frameworks. Problems that are way beyond any biological brain include: protein folding; stem cell behaviours; drug interactions; the understanding of chemistry, biology, seismic activity, and weather systems, pollution and global warming; plus the creation of new materials, device, machine and building design. Presented @ The University of Essex Innovation Centre for the IoD It also turns out that they are essential for the creation of sustainable societies…

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Engineering might be defined as the judicial application of science and scientific knowledge, but with the rider that unlike science and scientific studies, engineering always has to deliver a solution and a result. There are therefore aspects of engineering that stretch and challenge, the accepted, wisdom and knowledge of science. To purists, this might appear outrageous, but it is no more so than the works of Erwin Schrödinger or Leonhard Euler et al In this lecture we examine many of the established engineering basics whilst being mindful that most of our education, techniques, and working solutions are founded on the assumption of well behave linear environments. As our entire universe, and everything in it, is inherently complex and non-linear, we have to salute the powers of approximation and iteration for our many engineering success to date. However, we are increasingly being challenged by complexities of the fundamental non-linear nature of the problems confronting us. ( E.G. Politics, Conflict, Global Warming, Sustainability, Medicine, Fusion Power, Logistics, Networks, Depletion of Resources, Accelerating Tech Driven Change +++) We start by tracing history from the foundations up to the present day, including modern analytical nomenclature and techniques, system reliability, resilience and costs, we highlight the the basic human limitations that necessitate multi-disciplinary teams that include AI and vast computing power. The overall treatment includes our analogue past, digital today, and analogue/digital hybrid future of computing, robots, networks and systems of all kinds. It also includes animations, movies and sound files to demonstrate the realities of modern system design including the inherent complexities. To further highlight, and exemplify this projected future, we examine a real engineering project concerned with acoustic sniper spotting under battlefield conditions and extreme noise. Here a combination of digital modelling sees the use of analogue acoustic filter arrays, analogue signal amplification, and digital signal processing doubling the range of sniper detection and location.

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Seventy years on from AI appearing on the public scene and all the optimistic projections have been largely overtaken with systems outgunning humans at all board, card and computer games including Chess, Poker and GO. Of course; general knowledge, medical diagnosis, genetics and proteomics, image and pattern recognition are now all firmly in the grasp of AI. Interestingly, AI is treading a similar path to computing in that it began with single purpose/task machines that could only deal with a company payroll calculations or banking transactions and nothing more! General purpose computing emerged over further decades to give us the PCs and devices we now enjoy. So, AI currently runs as task specific applications on these general purpose platforms, and no doubt, general purpose AI will also become tractable in a few decades too! Recent progress has promoted a deal of debate and discussion along with hundreds of published papers and definitions that attempt to characterise biological and artificial intelligence. But they all suffer the same futility and fail! Without reference to any formal characterisation, all discussion and debate remains relatively meaningless. Somewhat ironically, it was the defence industry that triggered the analysis work here. Two of key steps to success were: the abandonment of all performance comparisons between biological and machine entities; and the avoidance of using the human brain as some ‘golden’ intelligence reference. This presentation is suitable for professionals and public alike, and comes fully illustrated by high quality graphics, animations and movies. Inevitably, it contains (engineering) mathematics that non-practitioners will have to take on trust, whilst professionals may wish challenge on the basis that the focus on getting a solution rather than the purity of the process!

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