Another ramification of the transition of the the R&E portion of Internet connectivity from the NSFNET service into a commercial marketplace is the need for a mechanism to compare the quality of service providers. Rather than procured via collaborative undertakings between the federal government and academia and industry, services in today's internetworking environments will be market commodities. Effective provision of those commodities will require the ability to describe Internet workload using metrics that will enable customers and service providers to agree on a definition of a given grade of service. Furthermore, metrics for describing the quality of connectivity will be important to market efficiency since they will allow customers to compare the quality of service providers when making procurement decisions.
In addition, users will want to be able to reserve bandwidth resources, which will require that the network provider have an understanding of the current traffic behavior in order to efficiently allocate reservations without leaving unused reserved bandwidth unnecessarily idle.
However the research community has yet to determine such metrics, and the issue requires immediate attention. A precursor to developing common metrics of traffic workload is a greater understanding of network phenomena and characteristics. Insight into workload requires tools for effective visualization, such as representing the flow of traffic between service providers, or across national boundaries. Without such tools for monitoring traffic behavior, it is difficult for an Internet service provider to do capacity planning, much less service guarantees.
In addition to our studies of operational statistics, we have also undertaken several studies that collect more comprehensive Internet traffic flow statistics and we have developed a methodology for describing those flows in terms of their impact on an aggregate Internet workload. We have developed a methodology for profiling Internet traffic flows which draws on previous flow models [3,17], and developed a variety of tools to analyze traffic based on this methodology. We think that NAP and other network service providers would gain great insight and engineering advantage by using similar tools to assess and track their own workloads.