The economics of the Internet: Utility, utilization, pricing, and Quality of
`Service
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`Andrew Odlyzko
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`AT&T Labs - Research
`amo@research.att.com
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`July 7, 1998.
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`Abstract. Can high quality be provided economically for all transmissions on the Internet? Current
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`work assumes that it cannot, and concentrates on providing differentiated service levels. However,
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`an examination of patterns of use and economics of data networks suggests that providing enough
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`bandwidth for uniformly high quality transmission may be practical. If this turns out not to be possible,
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`only the simplest schemes that require minimal involvement by end users and network administrators
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`are likely to be accepted. On the other hand, there are substantial inefficiencies in the current data
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`networks, inefficiencies that can be alleviated even without complicated pricing or network engineering
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`systems.
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`1. Introduction
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`The Internet has traditionally treated all packets equally, and charging has involved only a fixed
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`monthly fee for the access link to the network. However, there are signs of an imminent change.
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`There is extensive work on provision of Quality of Service (QoS), with some transmissions getting
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`preferential treatment. (For a survey of this area and references, see the recent book [FergusonH].)
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`Differential service will likely require more complicated pricing schemes, which will introduce yet
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`more complexity.
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`The motivation behind the work on QoS is the expectation of continued or worsening congestion.
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`As Ferguson and Huston say (p. 9 of [FergusonH])
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`...
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`it sometimes is preferable to simply throw bandwidth at congestion problems. On a
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`global scale, however, overengineering is considered an economically prohibitive luxury.
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`Within a well-defined scope of deployment, overengineering can be a cost-effective alter-
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`native to QoS structures.
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`The argument of this paper is that overengineering (providing enough capacity to meet peak de-
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`mands) on a global scale may turn out not to be prohibitively expensive. It may even turn out to be the
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`cheapest approach when one considers the costs of QoS solutions for the entire information technolo-
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`gies (IT) industry.
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`Overengineering has been traditional in corporate networks. Yet much of the demand for QoS is
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`coming from corporations. It appears to be based on the expectation that overengineering will not be
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`feasible in the future. “There's going to come a time when more bandwidth is just not going to be
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`available... and you'd better be able to manage the bandwidth you have,” according to one network
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`services manager [JanahTD].
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`The abandonment of the simple traditional model of the Internet would be a vindication for many
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`serious scholars who have long argued that usage-sensitive pricing schemes and differential service
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`would provide for more efficient allocation of resources. (See [McKnightB] for references and surveys
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`of this work.) The need for usage-sensitive pricing has seemed obvious to many on the general grounds
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`of “tragedy of the commons”. As Gary Becker, a prominent economist, said recently (in advocating
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`car tolls to alleviate traffic jams and the costs they impose on the economy [Becker]):
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`An iron law of economics states that demand always expands beyond the supply of free
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`goods to cause congestion and queues.
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`It may indeed be an iron law of economics that demand for free goods will always expand to
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`exceed supply. The question is, will it do so anytime soon? An iron law of astrophysics states that the
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`Sun will become a red giant and expand to incinerate the Earth, but we do not worry much about that
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`event. Furthermore, the law of astrophysics is much better grounded in both observation and theoretical
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`modeling than the law of economics. For example, consider Table 1 (based on data from tables 12.2
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`and 18.1 of [FCC]). It shows a dramatic increase in total length of toll calls per line. Such calls are paid
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`by the minute of use, and their growth was presumably driven largely by decreasing prices, as standard
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`economic theory predicts. On the other hand, local calls in the U.S. (which are almost universally
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`not metered, but paid for by a fixed monthly charge, in contrast to many other countries) have stayed
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`at about 40 minutes per day per line in the last two decades. The increase of over 62% in the total
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`volume of local calls was accompanied by a corresponding increase in the number of lines. There is
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`little evidence in this table of that “iron law of economics” that causes demand to exceed supply, and
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`which, had it applied, surely should have led to continued growth in local calling per line. (There is also
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`little evidence of the harm that Internet access calls are supposed to be causing to the local telephone
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`companies. This is not to say there may not have been problems in some localities in California, for
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`example, or that there won' t be any in the future. However, at least through 1996 the increasing use of
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`networked computers has not been a problem in aggregate.)
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`An obvious guess as to why we have stable patterns of voice calls is that people have limited time,
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`and so, with flat-rate pricing, their demand for local calls had already been satisfied by 1980. However,
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`that is not what the data in Table 1 shows. While the total volume of local calls went up almost
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`63% between 1980 and 1996, population increased only 16.5%, so minutes of local calls per person
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`(including modem and fax calls) increased by 40%. Thus demand for local calls has been growing
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`vigorously, but it was satisfied by a comparable increase in lines. Families and businesses decided, on
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`average, to spend more on additional phone lines instead of using more of the “free good” that was
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`already available. Somewhat analogous phenomena appear to operate in data networking, and may
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`make it feasible to provide high quality undifferentiated service on the Internet.
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`In data networks, at first sight there does appear to be extensive evidence for that “iron law of
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`economics.” Comprehensive statistics are not available, but it appears that Internet traffic has been
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`doubling each year for at least the last 15 years, with the exception of the two years of 1995 and 1996,
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`when it appears to have grown by a factor of about 10 each year [CoffmanO]. Almost every data link
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`that has ever been installed was saturated sooner or later, and usually it was sooner rather than later.
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`An instructive example is that of the traffic between the University of Waterloo and the Internet, shown
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`in Fig. 1. The Waterloo connection started out as a 56 Kbps link, was upgraded to 128 Kbps in July
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`1993, then to 1.5 Mbps in July 1994, and most recently to 5 Mbps in April 1997 [Waterloo]. Based on
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`current usage trends, this link will be saturated by the end of 1998, and will need to be upgraded, or
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`else some rationing scheme will have to be imposed. (A partial rationing scheme is already in effect,
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`since the link is heavily utilized and often saturated during the day.)
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`The University of Waterloo statistics could be regarded as clinching the case for QoS and usage-
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`sensitive pricing. They show a consistent pattern of demand expanding to exceed supply. However,
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`I suggest a different view. The volume of data traffic in Fig. 1 grows at a regular pace, just about
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`doubling each year. The 12-fold jump in network bandwidth from 128 Kbps to 1.5 Mbps in July 1994
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`did not cause traffic to jump suddenly by a factor of 12. Instead, it continued to grow at its usual pace.
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`The students did not go wild, and saturate the link by downloading more pictures. Similarly, statistics
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`for traffic on the Internet backbones show steady growth, aside from an anomalous period of extremely
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`rapid increase in 1995 and 1996 [CoffmanO], and the NSFNet backbone in particular had traffic almost
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`exactly doubling from the beginning to 1991 to the end of 1994. And should an increase in traffic be
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`wrong? We are on the way to an Information Society, and so in principle we should expect growth in
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`data traffic.
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`How much capacity to provide should depend on the value and the price of the service. To decide
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`what is feasible or desirable, we have to consider the economics of the Internet. Unfortunately, the
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`available sources (such as those in the book [McKnightB] or those currently available online through
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`the links at [MacKieM, Varian]) are not adequate. The information they contain is often dated, and it
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`usually covers only the Internet backbones. However, these backbones are a small part of the entire
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`data networking universe. Sections 2 to 6 attempt to partially fill the gap in published information about
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`the economics of the Internet.
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`Fig. 2 is a sketch of the Internet, with the label “Internet” attached just to the backbones (as the
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`term is often used). As will be shown in Section 2 (based largely on the companion papers [CoffmanO,
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`Odlyzko2]), these backbones are far smaller than the aggregate of corporate private line networks,
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`whether measured in bandwidth or cost (although not necessarily in traffic). (See Table 2 for the sizes
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`of data networks in the U.S. It is taken from [CoffmanO], and effective bandwidth, explained in that
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`reference, compensates for most data packets traveling over more than a single link.) The private line
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`networks, in turn, are dwarfed by the LANs (local area networks) and academic and corporate campus
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`networks. Most of the pricing and differentiated service schemes that are being considered, though, are
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`aimed at Internet backbones or private line WAN links. We need to consider how they would interact
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`with the other data networks and the systems and people those networks serve.
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`Most of the effort on QoS schemes is based on the assumption of endemic congestion. However,
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`when we examine the entire Internet, we find that most of it is uncongested. That the LANs are lightly
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`used has been common knowledge. However, it appears to be widely believed that long distance data
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`links are heavily utilized. The paper [Odlyzko2] (see Section 3 for a summary) shows that this belief
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`is incorrect. Even the backbone links are not used all that intensively, and the corporate private line
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`networks are very lightly utilized. There are some key choke points (primarily the public exchange
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`points, the NAPs and MAEs, and the international links) that are widely regarded as major contributors
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`to poor Internet performance, but there is even some dispute about their significance.
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`(In general,
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`while there have been numerous studies of the performance of the Internet, some very careful, such as
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`[Paxson], there is still no consensus as to what causes the poor observed performance.)
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`What is not in dispute is that a large fraction of the problems that cause complaints from users are
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`not caused by any deficiencies in transmission. Delays in delivery of email are frequent, but are almost
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`always caused by mail server problems, as even trans-Atlantic messages do get through expeditiously.
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`A large fraction of Web-surfing complaints are caused by server overloads or other problems. There
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`are myriad other problems that arise, such as those concerned with DNS, firewalls, and route flapping.
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`A key question is whether QoS would help solve those other problems, or would aggravate them, by
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`making the entire system more complicated, increasing the computational burden on the routers, and
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`increasing the numbers and lengths of queues.
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`Many QoS schemes require end-to-end coordination in the network, giving up on the stateless
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`nature of the Internet, which has been one of its greatest strengths. Essentially all QoS schemes have the
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`defect that they require extensive involvement by network managers to make them work. However, it is
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`already a major deficiency of the Internet that, instead of being the dumb network it is often portrayed
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`as, it requires a huge number of network experts at the edges to make it work [Odlyzko3]. Instead of
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`throwing hardware and bandwidth at the problem, QoS would require scarce human resources.
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`The evidence presented in this paper, combined with that of [Odlyzko2], shows that the current
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`system, irrationally chaotic as it might seem, does work pretty well. There appear to be only a small
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`number of choke points in the system, which should not be too expensive to eliminate. Further, there
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`are some obvious inefficiencies in the system that can be exploited. By moving away from private lines
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`to VPNs (Virtual Private Networks) over the public Internet, one could provide excellent service for
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`everybody through better use of aggregation of traffic and complementarity of usage patterns. The bulk
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`of the work on QoS may be unnecessary.
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`Anania and Solomon wrote a paper in 1988 (which was widely circulated and discussed at that
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`time, but was only published recently in [AnaniaS]) that took the unorthodox approach of arguing
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`for a flat-rate approach to broadband pricing. That paper was about pricing of what are now called
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`ATM services, which have QoS built in, but many of Anania and Solomon's arguments also imply
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`the desirability of a simple undifferentiated service. My work presents some additional arguments and
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`extensive evidence of the extent to which the traditional undifferentiated service, flat-price system can
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`work.
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`QoS does have a role to play. There will always be local bottlenecks as well as emergency situations
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`that will require special treatment. Even when local network and server resources are ample, there
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`will often be need to ration access to scarce human resources, such as technical support personnel.
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`Even in the network, methods such as Fair Queueing [FergusonH] can be valuable in dealing with
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`local traffic anomalies, for example. Implementing them would represent a departure from the totally
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`undifferentiated service model, but a mild one, and one that can be implemented inside the network,
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`invisible to the users, and without requiring end-to-end coordination in the network. My argument is
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`that we need to make the network appear as simple as possible to the users, to minimize their costs.
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`Sections 2 through 12 describe the economics of the Internet. The conclusion is that with some ex-
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`ceptions, the system does work pretty well as is. There are bottlenecks, but there are also inefficiencies
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`that can be exploited to eliminate the bottlenecks. Users in general behave sensibly, and although their
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`demands for bandwidth are growing rapidly, these demands are reasonably regular and predictable. It
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`appears likely that unit prices for transmission capacity will decline drastically (although total spending
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`on high bandwidth connections will surely grow), which should make it economically feasible to meet
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`the growing demand.
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`It is impossible to predict with any certainty how the Internet will evolve, especially since its evo-
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`lution depends on many factors, not only basic computing and networking technology and possible
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`appearance of the proverbial “next killer app,” but also on government regulation and sociology. Still,
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`some conclusions can be drawn from the study of the current system. The complexity of the entire In-
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`ternet is already so great, that the greatest imperative should be to keep the system as simple as possible.
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`The costs of implementing involved QoS or pricing schemes are large and should be avoided. Section
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`13 outlines three scenarios that appear most likely. One is the continuation of the current flat rate pric-
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`ing structure with almost uniform best-effort treatment of all packets, and enough bandwidth to provide
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`high quality transmission. That scenario is likely to materialize if transmission prices decline rapidly
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`enough. If they don't, the second scenario might arise, still with flat rate pricing and undifferentiated
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`service, but with pricing reflecting expected usage of a customer. Finally, if even greater constraints
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`are needed on traffic, ones that would provide congestion controls, approaches such as the Paris Metro
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`Pricing (PMP) scheme of [Odlyzko1] might have to be used. PMP is the least intrusive possible usage-
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`sensitive pricing scheme possible, and my prediction is that if any usage-sensitive pricing is introduced,
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`it will eventually evolve towards (or degenerate into) PMP. None of these three scenarios would meet
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`the conventional standards for economic optimality. However, the main conclusion of this paper is that
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`optimality is unattainable, and we should seek the simplest scheme that works and provides necessary
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`transmission quality.
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`2. The Internet and other networks
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`There are many excellent technical books and journal articles describing the technologies of the
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`Internet (cf. [Keshav]). There is also a huge literature on how the Internet will change our economy
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`and society (cf. [Gates]). On the other hand, practically nothing has been published on how the Internet
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`is used, and how much it costs. It is as if we had shelves full of books telling us how to build internal
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`combustion engines, and a comparable set of books on the effects of the automobile on suburban sprawl,
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`income inequality, and other socioeconomic issues, but nothing about how many cars there were, or
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`how much they cost to operate.
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`This section attempts to partially fill this gap in the knowledge of economics of data networks, but
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`the picture it presents can only be a sketchy one. Still, it should help illuminate the major economic
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`factors that are driving the evolution of the Internet.
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`The Internet (sometimes called the global Internet) refers to the entire collection of interconnected
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`networks around the world that share a common addressing scheme. As such, it includes all of the
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`elements shown in Fig. 2, which is a grossly simplified sketch of the data networking universe. The
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`element called “Internet” in Fig. 2 is really just the public Internet, the core of the network consisting of
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`the backbones and associated lines that are accessible to general users. WANs (Wide Area Networks)
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`consist of some of the clouds in that figure (which are made up of LANs and campus networks) con-
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`nected via either private line networks, or via public Frame Relay and ATM data networks provided by
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`telecommunications carriers, or else via the public Internet. Fig. 2 omits many important elements of
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`the data networking universe, such as regional ISPs.
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`This paper will concentrate on data networks in North America, primarily in the United States. Just
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`as in the companion papers [CoffmanO, Odlyzko2], the justification is that most of the spending on
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`data traffic is in the U.S. [DataComm]. Further, U.S. usage, influenced by lower prices than in most of
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`the world [ITU], foreshadows what the rest of the world will be doing within a few years, as prices are
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`reduced.
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`Data networks do not operate in isolation. To see them in the proper perspective, let us note that total
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`spending on information technologies (IT) in the U.S. was about $600 billion in 1997, approximately
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`8% of gross domestic product. The IT sector of the economy is credited with stimulating the high
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`growth rate of the economy of the last few years, low unemployment, and low inflation [DOC].
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`Data communications cost about $80 billion in the U.S. in 1997, or 13% of total IT spending,
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`according to [DataComm]. Table 2 is a brief summary of the statistics on where this spending was
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`directed, based on the more detailed information in [DataComm] (which also covers the rest of the
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`world). These statistics show that transmission accounted for only $16 billion, 20% of total for data
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`communications, and 2.6% of total for all of IT. Thus data lines are a small part of the entire IT picture,
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`and any scheme that attempts to improve their performance has to be weighed against costs that it might
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`impose on the rest of the system. It is better to double the spending on transmission than to increase
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`the average cost of all other IT systems by 3%.
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`Let us also note that U.S. spending on phone services from telecommunications carriers was around
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`$200 billion in 1997. Of this total, about $80 billion was for long distance calls, but about $30 billion
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`was for access charges, paid to the local carriers. Thus it is more appropriate to say that $50 billion was
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`for long distance services, and $150 billion for local services. In any event, today much more is being
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`spent on voice phone services than on data. In the future, as broadband services grow, we can expect
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`the balance to shift towards data. In particular, looking at total communications spending and how it is
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`still dominated by voice, it is reasonable to expect substantial growth in spending on data transmission.
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`The core of the Internet, namely the backbones and their access links, is surprisingly inexpensive.
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`There are many large estimates for total Internet spending, but those are misleading. There were about
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`20 million residential accounts with online services such as AOL at the end of 1997. At $20/month,
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`they generated revenues of around $5 billion per year. However, most of that revenue is used to cover
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`local access costs (the modems, customer service, and marketing expenses of the ISPs). The back-
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`bones are only a small part of the cost picture for residential customers (cf. [Leida]). In the statistics
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`of [DataComm] (and of Table 2) they apparently are included in the ”Commercial Internet services”
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`category, which came to $1.5 billion in 1997. We now derive two other estimates that are both in that
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`range. According to industry analysts [IDC], MCI's Internet revenues (which include only a small
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`contribution from residential customers, and are dominated by corporate and regional ISP links to the
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`MCI network) came to $251 million in 1997 (a 103% increase over 1996), and were running at an
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`annual rate of $328 million in the last quarter of 1997. Since MCI is estimated to carry between 20
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`and 30 percent of the backbone traffic, we can estimate total revenues from all backbone operations
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`between $1.1 and $1.6 billion at an annual rate at the end of 1997. (With revenues doubling each year,
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`it is not adequate to look at annual statistics.) Yet another, rough way to estimate the costs of Internet
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`backbones is to take their size, around 2,100 T3 equivalents at the end of 1997 [CoffmanO], and apply
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`to that the $20,000 per month average cost of a T3 line [VS]. This produces an estimate of about $500
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`million per year for the main backbone links. When we add some additional costs for the access lines
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`from carriers' Points of Presence to their backbones (cf. [Leida]), and apply the general estimate that
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`for large carriers, transmission costs are about half of total costs, we arrive at an estimate of about $1.5
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`billion for the costs of the core of the Internet. (Costs and revenues are not the same, especially in the
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`Internet arena, where red ink is plentiful as various players attempt to build market share, but within
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`the huge uncertainty bounds we are working with, that should not matter much.)
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`Compared to the Internet backbones, the total cost of private line networks is at least 6 times as
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`large. Furthermore, the aggregate bandwidth of leased lines is also much greater than of the public
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`backbones, although the traffic they carry appears to be comparable in volume. (See tables 2 and 5,
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`taken from [CoffmanO].) This helps explain why the evolution of the Internet is increasingly dominated
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`by corporate networks.
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`Just as with switched voice networks, data network costs are dominated by the local part. However,
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`there is much more heterogeneity in data than in voice.
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`In the foreseeable future, large academic
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`and corporate networks are likely to have 2.4 Kbps wireless links along with 14.4 and 28.8 Kbps
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`modems, megabit xDSL and cable modem links, and gigabit fiber optic cables. In the local campus
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`wired environment, overengineering with Ethernet, Fast Ethernet, Gigabit Ethernet, and similar tools
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`appears certain to be the preferred solution. However, there will still be challenges of interconnecting
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`the other transmission components (whether slower or faster), as well as all the servers and other
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`equipment that require the bandwidth. Network managers will have a hard time making everything
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`interoperate satisfactorily even without worrying about QoS.
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`The Internet backbones are small and inexpensive compared to the rest of the Internet. However,
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`they are the heart of the Internet, just like a human heart that is small but crucial for the life of the
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`body. The role of the backbones will likely become even more important in the future as a result of
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`several related developments. One is that they are being traversed by an increasing fraction of data
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`traffic. The traditional 80/20 rule, which said that 80% of the traffic stayed inside a local or campus
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`network is breaking down. Ferguson and Huston [FergusonH] even mention some networks where as
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`much as 75% of the traffic goes over long distance links. (We do not know how far that traffic goes,
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`and in particular whether there will continue to be a strong distance dependence in the future. See
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`[CoffmanO] for a more detailed discussion.) Another reason for the increasingly important role of the
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`Internet backbones is that they are supplanting private line networks as corporate WAN links, and with
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`the development of extranets, will be playing a crucial role in the functioning of the whole economy.
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`Thus there is a reason to worry about the costs of the backbones, as they might become a larger fraction
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`of the total networking pie. On the other hand, if one can overengineer only one part of the Internet,
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`then it is best to do it to the core, as without high quality transmission at the core, other parts of the
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`network will be only be able to offer poor service.
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`3. Network utilization
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`Network utilization rates are seldom discussed, yet they are the main factor determining costs of
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`data services. A line that is used at 5% of capacity costs twice as much per byte of transmitted data as
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`one whose average utilization rate is 10%.
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`Although there is no simple relation between the quality perceived by customers and how heavily
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`their networks are used, the less heavily loaded the network, the better the service. Even the notoriously
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`congested trans-Atlantic links do appear to provide good performance for applications as demanding
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`as packet telephony in the early hours of Sunday morning. What this says is that even without any new
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`QoS technologies, one can provide excellent quality by lowering utilization. Thus the main problem of
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`the Internet is not a technical one, but an economic one, whether one can afford to have lightly utilized
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`networks. As an example, the experimental vBNS network, discussed in [Odlyzko2], provides quality
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`sufficient for even the most demanding applications, but it is expensive (or would be expensive, were it
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`operated on a commercial basis), running at an average utilization of its links of around 3%.
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`The main question for the future of the Internet is whether customers are willing to pay for high
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`quality by having low utilization rates, or whether many links will be congested, with QoS providing
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`high quality for some select fraction of data transfers. We can find much about the likely evolution
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`of data networks from observation of usage patterns of existing networks. When we consistently see
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`lightly utilized links where customers can obtain higher utilization rates and lower costs by switching
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`to lower capacity lines, we can deduce that they do want high quality data transport, and are willing to
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`pay for it.
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`Table 4 shows utilization rates (averaged over a full week) for various networks. It is based on
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`[Odlyzko2], except for the entry for local phone lines, which is derived from the data in Table 1 (which
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`is based on [FCC]). A surprising result is that the long distance switched network is by far the most
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`efficient in terms of utilizing transmission capacity. For most people, an even more surprising feature
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`of the data is the low utilization rate of private line networks.
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`The paper [Odlyzko2] discusses the reasons data networks are lightly utilized. Lumpy capacity is a
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`major one. Rapid and unpredictable growth is another. Small private networks are yet another. Perhaps
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`the main reason, though, is the bursty nature of data traffic. This traffic is bursty on both the short
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`and long time scales, and customers do value such bursty transmission. This means that we cannot
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`reasonably expect data networks to approach the efficiency with which the switched voice network
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`uses transmission capacity.
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`Utilization rates can also provide guides to the extent that QoS measures might improve perceived
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`quality of networking. Practically all Internet users find service much better 5 in the morning than at
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`noon. However, traffic on the backbones in the early hours of the morning is still about half that during
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`the peak hours, as is seen in Fig. 10 of this paper and several figures in [Odlyzko2]. Further, traffic
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`mix does not seem to vary much between trough and peak periods [ThompsonMW]. Therefore we
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`can conclude that during peak periods, no QoS measure is likely to provide transmissions that have
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`priorities around the median of all traffic with better service than the current undifferentiated service
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`provides for all traffic early in the morning. (There could be some improvements in jitter, for example,
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`but algorithms that do provide such improvements could be used inside the network, invisibly to the
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`users, to provide similar improvements for undifferentiated service.)
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`An important point in considering the low utilization rates of data networks is that it is not caused
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`primarily by inefficiency or incompetence. Customers choose the capacities of their lines, and their
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`choices tell us what they want and what they are willing to pay for. This point will be treated at greater
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`length in the following sections.
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`4. Inefficiency is good (if you can afford it)
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`Efficiency in utilization of transmission lines or switches should not be the main criterion for
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`evaluating how good a network is. The crucial question is how well customers' needs are satisfied.
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`Consider figures 3 and 4, which show utilizations of dial-in modems at Columbia University and
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`the University of Toronto. (These figures are based on detailed data supplied by those institutions.
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`Graphs for more recent periods, separated out further by 14.4 and 28.8 Kbps modem pools, can be
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`found at [Columbia, Toronto].) The average utilizations (over the periods shown in the figures) were
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`52% at the University of Toronto and 78% at Columbia University. Clearly Columbia was utilizing its
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`modems more efficiently. Was it providing better service, though? Its modems were completely busy
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`for more than 12 hours a day. (The slight drops below 100% in the utilization in Fig. 3 are misleading,
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`since they represent only a little idle time, and are largely the resetting of modems after a session is
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`terminated.) Clearly much demand is unsatisfied, and there are many frustrated potential users who do
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`not accomplish their work. Further, the high 78% utilization rate is misleading, since many users are
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`probably staying online for longer periods than they would if they had assurance they could get a new
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`connection when they wanted it. Toronto, with a lower utilization rate, managed to accommodate all
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`demands except for a brief period on Monday night.
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`The University of Toronto managed to satisfy essentially all demands of its students and faculty
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`for modem connections and still achieve a 52% utilization rate. This rate is extremely high. There are
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`many examples of low utilization rates. The family car is typically used around 5% of the time. The
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`fax in our office or home, the PC on the desktop, and the road we drive on are all designed for peak
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`usage, and are idle most of the time. We are willing to pay for this inefficiency because the costs are
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`low compared to the benefits we receive. As costs decrease, we usually accept lower efficiency. For
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`example, in the early days of computing, programs were written in assembly language. Later, as the
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`industry advanced, there was a shift towards compiled programs. They typically run at half the speed
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`11
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`

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`of assembly coded versions, but they make software easier to write and more portable. With further
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`advances in computing power, industry was willing to jump on the Java bandwagon, even though the
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`early versions of Java typically ran a hundred times slower than compiled programs. With over 99% of
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`computing cycles devoted to running screen savers, this was a worthwhile tradeoff.
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`When capital cos