Telecoms Futurology Limits and Constraints

© Martin Geddes Consulting Ltd November 2014

“It is always wise to look ahead, but difficult to look further than you can see.”

— Winston Churchill

What makes for a good prediction of the future?

Futurism health warning

Some aspects of the future are “like today, just with the volume turned up.”

But the nature of technology also involves second-order changes, as society re-organises around new technology. For example, the automobile led to shopping malls and suburbia.

Over time, the focus shifts from the functions of the technology itself towards novel applications that build upon its assumed existence. The widespread growth of drive-through restaurants was only possible once there was a ubiquitous and cheap infrastructure for all those automobiles.

Further applications and higher-order change becomes possible as previous generations become modular components of new ones.

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Futurism health warning

The underlying original technology becomes increasingly invisible, like the motor in a toothbrush. That broadband and computing become ubiquitous and embedded in this way is relatively certain.

In contrast, the combinatorial nature of application innovation means huge uncertainty is introduced into any application forecast.

The context also shapes the need. Negative shocks like natural disasters, or positive ones like the invention of the smartphone, can quickly re-shape demand.

The future is also malleable, and we ourselves can help to choose which innovations to accelerate.

“It is not in the stars to hold our destiny but in ourselves.” — William Shakespeare

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End-of-history illusion

Looking back, it’s easy to see the sweep of

history, both in our own lives, as well as in

society and technology.

Looking forward, it’s much harder to project ourselves into a world that changes every bit as much, if not more.

We therefore typically

under-estimate the amount of individual and collective change

we will experience.

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This is particularly true today, as the speed of technology change is also increasing due to

‘accelerating returns’. This needs to be factored in: ‘incredible’ futures are credible

even on a 10-year timescale.

7 Source: Wikipedia/Kurzweil

So how much change can or will occur?

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Constraints divide the possible and impossible

Why don’t we have flying cars? It is because there are constraints that have prevented our dreams from becoming widespread reality.

Thinking of communications, consider technological constraints first. We are used to seeing processing power, storage and data transmission rates increase exponentially over many decades.

However, progress in machine intelligence doesn’t necessarily happen at the rate of Moore’s Law. For instance, hundreds of PhDs slowly grind away at problems like automatic speech recognition.

Conceptual innovation is relatively slow.

Understanding these constraints is key to making realistic forecasts.

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Cosmic Ludic Ecological Physics

Chemistry Mathematics Technology

Economics Policy

Three basic kinds of constraints

Source: Nassim Taleb 10

Cosmic Ludic Ecological High-frequency

trading is limited by speed of light.

Conservation law: quantity sold = bought;

you can’t create new stocks to cover your losses.

Insider trading is illegal.

There are fees to pay for trading.

Example: the stock market

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Cosmic Ludic Ecological

Speed of light, RF interference,

quantum uncertainty, energy conservation,

Shannon’s limit

Statistical multiplexing,

proof of software correctness

Optoelectronics, heat management,

staff availability, regulatory reporting,

anti-fraud governance

Examples of constraints on telecoms services

We will look at some key constraints whose impact is typically underestimated

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Network Science is a key constraint

The theoretical foundations of statistical resource sharing – the very essence of packet data – are absent from common network engineering practice.

Our usual approach of “theory is no substitute for experience” has resulted in a basic science deficit. This leaves network performance engineering as a (highly skilled) craft.

This leaves us vulnerable to unpleasant surprises as we attempt to scale systems, since past success is no guide to future hazards.

“Structural engineering” failures are relatively common for broadband networks and applications.

Basic processes are manual, such as fault isolation. Contracting performance across network or organisational boundaries is immature.

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Quality and performance are technical constraints

As a result of this science deficit, the telecoms industry has a general and widespread issue with performance management. It struggles to manufacture, package, price and market quality in a packet world.

Standards to measure and manage often fall short. They don’t adequately reflect the complex reality of application demand or network supply.

Services also need to work both on-net and off-net. That means there are assurance interoperability requirements. IMS adds the complexity of legacy circuit models on-net, yet still fails to deliver true assurance. IPX promises much in terms of internetwork quality interoperability, but delivers little thus far.

Hence there is an endemic struggle to manage quality and performance. How well this issue is addressed changes the base assumptions about what is possible, and who will thrive.

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Risks of virtualisation

These issues are going to get worse in virtualised network environments, which introduce more variability and complexity.

You can think of it as being the networking equivalent of metal fatigue. Extreme stress and vibration afflicted early jet aircraft, and it took years (and several major disasters) to understand and manage it.

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We can conceive of all kinds of amazing new services.

However, the complexity of their interactions with operational systems may overwhelm our ability to manage them.

The “lights out” automated network of the future would know what is supposed to be always true about its operation, and keep those things true. It would self-manage routine variation, and only involve humans in rare extreme events.

We are a very long way from this ideal. Legacy and complexity

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Source:

Issues of immature science and technology constraints are not unique to telecommunications.

Frameworks like Technology Readiness Levels (TRLs) have been developed to help assess risk and plan for the future.

Our assumption is that the new technologies that will be mainstream in ten years are the ones that are just entering the market now, or have working demonstrations built.

Those which are still in basic research are assumed to be greater than 10 years from mass commercialisation.

So no flying cars. Sorry.

Technology maturity

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Other constraints on progress

You can think of technologies as being players on a stage: a few are ‘leading actors’, the rest ‘supporting cast’. The products and services can only take off when they are all present and aligned. Once that alignment happens, the new services can take off very fast, as the speed at which services diffuse is increasing rapidly. Thus it is easy to be caught off-guard.

There are also behavioural , cultural and organisational limits to the pace of change. Legal and regulatory issues can also easily get in the way. These offer lots of possible ‘gotchta!’ issues: spectrum, interconnect, numbering, network neutrality, local vs State vs Federal rules, 911, reporting, tariffing, and so on.

On top of this there are macro-level uncertainties. Will the machine intelligence revolution follow the path of previous coal, electricity and oil ones to produce a Golden Age of abundance? Or will there be a Great Disconnection of wealth-production and general wellbeing? Nobody can say for sure.

Why list all these caveats? The interaction of all these factors is too complex to model. We can have a good directional guess where things will go, but the timing is very unpredictable.

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“If you do not change direction, you may end up where you are heading.”

— Lao Tzu

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