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Traffic and Everything Like Traffic Dealing with Network Performance Degradation

This White Paper explains network idiosyncrasies and degradations and discusses best practices to identify these network problems, leading to their resolution.

This White Paper is reprinted with permission from Apparent Networks.

If you were to treat each cause of performance degradation as a unique kind of problem, optimizing your network's performance becomes a near impossible task. Dealing uniformly with traffic, and everything that looks like traffic, makes it possible to locate and identify issues quickly. In the end, this Best Practice will greatly simplify the challenging job of troubleshooting your network.

On a typical IP network, traffic is anticipated as part of the design. Packets bound end-to-end, from one host to another, are expected to encounter traffic in the form of other packets sent between other hosts. Competing traffic impedes the exchange of packets, introducing delays and an overall reduction in available bandwidth. Extreme cases of traffic congestion can even result in packet loss. Therefore, traffic represents a form of network performance degradation, although reasonable levels of traffic are normal and tolerable. However, in cases where traffic conditions achieve a level of congestion, network paths may no longer function reasonably and the situation may demand some attention. In other cases, conditions may exist that introduce traffic-like effects, even if no traffic is present, degrading performance needlessly. These conditions can be identified and remedied.

The challenge then, in troubleshooting your network, is to detect traffic, distinguish it from other problems, determine if it is reasonable and the performance degradation inevitable. If it really is traffic, then you know any bandwidth you throw at your network is well spent. And if it isn't traffic, now you know - and you can just fix it.

This White Paper is posted in full as a pdf file. To continue reading, click here.

With a Ph.D. in computational physics from McGill University, Dr. Loki Jorgenson has been active in computation, physics and mathematics, visualization, and simulation for over 16 years. He has published in areas as diverse as philosophy, graphics, educational technologies, statistical mechanics, logic and number theory. Loki is an Adjunct Professor of Mathematics at Simon Fraser University, and he has headed research in numerous academic projects from high-performance computing to digital publishing, working closely with private sector partners and government.

Dr. Jorgenson brings his extensive experience to by offering advice to our members and assisting you with your network performance and monitoring issues -- ensuring that your infrastructure is working optimally from end to end. and Dr. Jorgenson welcome your comments, suggestions and feedback on this white paper. Let us know what you think! Send them here:

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