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On the surface, adding redundancy and reliability to a network should be no more difficult than solving the split-brain problem that we discuss in detail in 15, Local Clustering and Failover However, all of a host s resources are contained within or controlled by the system In the network, resources are highly distributed, have multiple points of control, and are subject to outside forces Network reliability poses several challenges to the system management team: Network failures come and go A period of very high traffic, followed by a lull in activity, followed by another peak, may appear to be a prolonged failure In this case, network latency and network failure produce the same client-side symptoms Measurement and monitoring tools must be able to distinguish between the two cases, and network segregation techniques provide isolation and protection against some forms of traffic-driven meltdowns.



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Naturally, the schemes in both chapters take account of the self-similar nature of the considered traf c ows They constitute alternatives to our own proposals 15 by Feldmann gives a very precise description of Internet traf c characteristics at ow level, which to some extent invalidates our too optimistic Poisson arrivals assumption The latter assumption remains useful, however, notably in showing how heavytailed distributions do not lead to severe performance problems if closed-loop control is used to dynamically share resources as in a processor sharing queue The same Poisson approximation is exploited by Boxma and Cohen in 6, which contrasts the performance of FIFO (open-loop control) and processor sharing (PS) (closed-loop control) queues with heavy-tailed job sizes In the next section we discuss the nature of traf c in a multiservice network, identifying broad categories of ows with distinct quality of service requirements.





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Open-loop and closed-loop control options are discussed in Sections 163 and 164, where it is demonstrated notably that self-similar traf c does not necessarily lead to poor network performance if adapted ow level controls are implemented A tentative service model drawing on the lessons of the preceding discussion is proposed in Section 165 Finally, in Section 166, we suggest how traditional approaches might be generalized to enable traf c engineering for a network based on this service model 162 THE NATURE OF MULTISERVICE TRAFFIC.

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It s hard to define where the network is A typical path from a PC to a server located in a machine room on the same campus will touch a switch or LAN concentrator in a local office wiring closet, a router or two in the same building, another router entering the data center building, and one or more machine room high-speed networking switches Add in network interface cards on PC, server, routers, and switches, and you have a dozen or more points of failure What if the backplane on a switch fails Or one outbound interface card on a router Each one of these pieces of equipment may be controlled by a different operations organization, adding a people angle to the complexity Latency contributes to perceived network failures A router that is overloaded or dropping packets in the middle of a long network path will greatly impact end-to-end reliability.

Avinash Kaushik had a one-month head start on Charlene Li in 2006 with the rst of a series of posts that were more operational and tactical in nature. While many blogging platforms will report on how many times your various posts were viewed, Avinash took a crack at measuring the number of people who read his blog and explained himself in a post called How to Measure Success of a Blog (120 Days in Numbers) (www.kaushik.net/avinash/2006/09/how-tomeasure-success-of-a-blog.html).

It is possible to identify an inde nite number of categories of telecommunications services, each having its own particular traf c characteristics and performance

Network design and traffic management help to reduce network latency, or at least control it, but it remains an external factor in service reliability measurement Denial-of-service attacks are real Most simply, a denial-of-service attack means that a user cannot get access to a host or network service because all of the input capacity is tied up dealing with bogus requests Here s a simple example using the ever-popular bank teller model: Five people simultaneously cash their paychecks, take the amount in pennies and unwrap them, then ask to deposit the pennies by counting them All other bank customers queued for a teller are stuck while the bogus requests are handled All real work is held pending while the annoyance requests are serviced..

requirements. Often, however, these services are adaptable and there is no need for a network to offer multiple service classes each tailored to a speci c application. In this section we seek a broad classi cation enabling the identi cation of distinct traf c handling requirements. We begin with a discussion on the nature of these requirements. 16.2.1 Quality of Service Requirements

State multicast, or reliable multicast techniques, are used to distribute updates from one process to all of its peer processes on the same server or on the same network. Implementations range from serial unicast, in which the updating process sends updates to each receiver in turn, to actual usage of Internet multicast class addresses with verification and logging to ensure that all recipients see all intended data. Most of the products in this space come from the middleware vendors such as Vitria/WebMethods, TIBCO, and BEA Systems.

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