The new science of complex systems is making a difference to networks, evident by a slew of new buzzwords:
- packet behaviors
- route analytics
- adaptive networks
- dynamic inference
- intelligent networks
- network-aware applications
- topology discovery
- progressive analytics
- emergent phenomena
Behind each phrase you'll find more than just marketing folks -- typically there are one or more PhDs and a handful of patents. So what's so new about all this?
For decades, network management was dominated by cumbersome, top-down infrastructures with small brains that emphasized devices, generated reams of data and charts, performed so-called root-cause analysis, and required full-time experts onsite. Expensive to buy, and even more expensive to maintain, such systems require constant upkeep and tuning. These NMS solutions promoted a fruitless "herding of cats" approach -- obvious in a device-centric era -- that, unfortunately, didn't work all that well.
In the meantime, networks continue to evolve and become even less susceptible to these "old world" methodologies. Now you don't own all the networks you use, user demands on networks have dramatically increased, the carriers and ISPs you depend on won't give you access to their infrastructure, it is "everything over IP," and it always seems like someone else's problem is your fault. You are left "managing problems" with your father's old NMS instead of actually solving them.
Adding fuel to the fire, the business case for networks has also changed dramatically. IT, in general, is no longer tolerated as an inevitable cost-center -- now it is expected to reduce costs or even generate new revenues. The cost of bandwidth has plummeted and the complexity of applications has sky rocketed. More and more vendors depend on networks, particularly those of their customers, and customer expectations have increased. You are expected to do more for less and actually make everything work. Right now.
Just as networks have evolved; network management is desperately in need of a change.
And that's where the new science comes in -- a fresh-faced bevy of start-up companies with fascinating technologies pursuing new approaches to dealing with networks. Some of that new science draws from heady areas of research like non-linear complex systems (remember the "butterfly effect"?) as well as:
- statistical mechanics
- expert systems
- optimization theory
- network tomography
- protocol informatics
- automated systems control
While you should be cautious about buying anything you don't understand -- it's encouraging to see networks finally leaving the manual/reactive Stone Age behind. Some companies working hard in this area include:
- Route Science (now part of Avaya)
- Wild Packets
- Network Physics
- Apparent Networks
- Packet Design
- Quantiva (now part of NetScout)
Here's a quick-test to help you decide if the new science matters to you: Are you more a fan of the Flintstones or the Jetsons?
Chief Scientist for Apparent Networks, Loki Jorgenson, PhD, has been active in computation, physics and mathematics, scientific visualization, and simulation for over 18 years. Trained in computational physics at Queen's and McGill universities, he has published in areas as diverse as philosophy, graphics, educational technologies, statistical mechanics, logic and number theory. Also, he acts as Adjunct Professor of Mathematics at Simon Fraser University where he co-founded the Center for Experimental and Constructive Mathematics (CECM). He has headed research in numerous academic projects from high-performance computing to digital publishing, working closely with private sector partners and government. At Apparent Networks Inc., Jorgenson leads network research in high performance, wireless, VoIP and other application performance, typically through practical collaboration with academic organizations and other thought leaders such as BCnet, Texas A&M, CANARIE, and Internet2. www.apparentnetworks.com
