In a previous post on the Honey-bee algorithm for allocating servers, which I found quite fascinating, I had pointed out I had referred to a paper on Swarm Intelligence by Eric Bonabeau and Christopher Meyer published by Harvard Business Review, and finally I got time to go back and read it and I found it quite fascinating! The paper describes case studies where people have used algorithms inspired by nature (ants, bees) which use a decentralized model of computation and optimization.
The paper points out that the main advantages of using algorithms like these are flexibility, robustness and self-organization. The algorithms work in a completely decentralized manner, and work on the principle that the wisdom of all the ants (or the small agents) can be harnessed in such a manner that the whole is far greater than the sum of its parts. Also, the algorithms are invariably robust to failure and adaptive since they don’t make use of a central decision making bodies and there is a lot of experimentation with new sources of food (or results in the case of algorithms).
The paper also points out that there are several cases where these concepts have been used successfully (both in business and academia):
- Optimally routing telephones calls and Internet data packets seems to be a tough problem because if we use a centralized algorithm, it will neither be robust nor adaptive. Algorithms based on swarm intelligence come to the rescue since they are not based on a central decision making body, but rather work on the principle that the scouts recruit other agents to follow new promising paths.
- Fleet management and cargo management also suffer from similar problems. The paper points out that Southwest Airlines found out that in some cases, letting cargo go to wrong destinations and recovering is faster and more robust than always making sure that all cargo is handled correctly.
- Small simple rules that lets people take decisions for themselves usually works best. This has since been shown to work very well for companies such as Google as well.
There are more case studies in the paper, but what’s fascinating is that these techniques become even more popular now-a-days because companies have realized that it is easier to tolerate failure than to eradicate it — more so in the context of the Internet where there is a race to build systems that are self-correcting (such as Map-Reduce, Hadoop and Dryad). Also the new realities of the emerging architectures (multi-core, highly parallel, massive clusters grids) is going to mean that we have more parallel horsepower to run our applications and such self-organizing algorithms are going to become even more popular in the computing community.
However, one concern would be programming models for such computing bedrocks. We still don’t understand how to manage parallel computation very well to ensure that interpreting such algorithms in code is going to remain difficult for the average programmer for quite sometime.