zed delay. The time it takes to stop and pay the toll is mere seconds, yet traffic often backs up for miles as a result of this local phe- nomenon, causing a global effect. This is the relationship between the two: Local delay often results in widespread congestion. And congestion is usually caused by inadequate buffer or memory space. Increase the number of buffers-the lanes of traffic on the bridge, if you will-and congestion drops off. Open another line or two at Home Depot ("No waiting on line seven!")-and congestion drops off. The various players in the fast-food industry manage congestion in different ways-and with dramatically different results. Without naming them, some use a single queue with a single server to take orders, a tech- nique that works well until the lunch rush begins. Then things back up dramatically. Others use multiple queues with multiple servers, a tech- nique that is better, except that one queue can experience serious delays should someone place an order for a nonstandard item or try to pay with a credit card. That line then experiences serious delay. The most effective restaurants stole an idea from the airlines, and use a single queue with multiple servers. This keeps things moving because the instant a server is available, the next person in line is served. Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright © 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website. Protocols Protocols Remember when Jeff Goldblum, the chaos theoretician in Jurassic Park, talked about the Butterfly Effect? How a butterfly flapping its wings in the Amazon Basin can kick off a chain of events that affect weather patterns in New York City? That aspect of Chaos Theory contributes greatly to the manner in which networks behave-and the degree to which their behavior is immensely difficult to predict under load. So how is congestion handled in data networks? The simplest technique, used by both f