Tag Archives: analytics

Generating Traffic Through Search

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Jevon’s Paradox

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I’ve been finding it necessary to quote Jevon’s paradox several times lately and realized that I have NOT referenced it here.  Quite simply, understanding Jevon’s paradox is essential to understanding cloud.

The concept of the paradox is that when we make something more efficient (for example gas in cars), the demand for that resource goes up (we move further into the exurbs because driving is cheaper).  Notably, as Moore’s law drives computer efficiency up, we are using more and more computers.  Specifically, I have more computers in my house every year even though the efficiency of just one smart phone far (far) exceeds power of my son’s Sinclair 1000.

In cloud computing, Jevon’s paradox points us to the expectation that the rush of applications and activity in the cloud will continue to accelerate.  Since I expect competition and Moore’s law to drive increasing gains in cloud efficiency (and therefore customer advantageous price signals) the market will happily convert these utilization improvements into more and more interesting capabilities.

The cloud expansion means that we can sustain more providers entering the market.  In fact, Jevon would tell us that more providers will likely INCREASE demand for cloud as competition and capacity put downward pressure on prices.  [Q: where will they make up the margin?   A: Adjacent Services]

The loser in cloud’s exploration of Jevon’s paradox are non-cloud deployments (Dell strategists are you listening?).  These systems suffer because their ability to improve their efficiency is limited.

As I look down the road on cloud, I can see many opportunities for current applications to take advantage of cheaper cloud resources to provide even more value.  For example, adding map-reduce analytics to scan a customer’s data can provide tremendous insights.  Today, it’s a luxury like flying on the Concorde.  Tomorrow, it will be part like hopping the Nerd Bird from San Jose to Austin – just a normal part of our daily lives.

 

Note: A shout out to Dave McCrory who introduced me to Jevon’s Paradox.

Are Clouds using Dark Cycles?

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Or “Darth Vader vs Godzilla”

Way way back in January, I’d heard loud and clear that companies where not expecting to mix cloud computing loads.  I was treated like a three-eyed Japanese tree slug for suggesting that we could mixing HPC and Analytics loads with business applications in the same clouds.  The consensus was that companies would stand up independent clouds for each workload.  The analysis work was too important to interrupt and the business applications too critical to risk.

It has always rankled me that all those unused compute cycles (“the dark cycles”) could be put to good use.  It’s appeals to my eco-geek side to make best possible use of all those idle servers.   Dave McCrory and I even wrote some cloud patents around this.

However, I succumbed to the scorn and accepted the separation.

Now all of a sudden, this idea seems to be playing Godzilla to a Tokyo shaped cloud data center.  I see several forces merging together to resurrect mixing workloads.

  1. Hadoop (and other map-reduce Analytics) are becoming required business tools
  2. Public clouds are making it possible to quickly (if not cheaply) setup analytic clouds
  3. Governance of virtualization is getting better
  4. Companies want to save some $$$

This trend will only continue as Moore’s Law improves the compute density for hardware.  Since our designs are leading towards scale out designs that distribute applications over multiple nodes; it is not practical to expect an application to consume all the power of a single computer.

That leaves a lot of lonely dark cycles looking for work.

Now all of a sudden, this idea seems to be playing Godzilla to a Tokyo shaped cloud data center.  I see several forces merging together to resurrect mixing workloads.

  1. Hadoop (and other map-reduce Analytics) are becoming required business tools
  2. Public clouds are making it possible to quickly (if not cheaply) setup analytic clouds
  3. Governance of virtualization is getting better
  4. Companies want to save some $$$

This trend will only continue as Moore’s Law improves the compute density for hardware.  Since our designs are leading towards scale out designs that distribute applications over multiple nodes; it is not practical to expect an application to consume all the power of a single computer.

That leaves a lot of lonely dark cycles looking for work.