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Our analysis of TCP connections is based on trace analysis of transmission control protocol=Internet protocol (TCP=IP) internetwork traf c. The traces were collected on three different Ethernet segments at Carnegie Mellon University (CMU), AT&T Bell Laboratories, and AT&T Labs Research using the tcpdump packet capture tool [24] running the Berkeley Packet Filter [28]. The number of packets that tcpdump reported dropped by the kernel was negligible. The rst set of traf c data was collected on an Ethernet segment of the School of Computer Science at CMU that is one of a total of 18 Ethernet segments that are bridged through a backbone with an aggregate 0.5 Gbit=s throughput. The workstation we used for the trace collection (DEC Alpha 400=300 with 64 Mbytes RAM) is connected to a segment that connects approximately 120 systems, including UNIX workstations, MacIntoshs, and PCs. The second traf c data set was collected on an Ethernet segment at AT&T Bell Laboratories. The third traf c data set was collected on the same Ethernet segment shortly after the split of AT&T Bell Laboratories into AT&T Labs Research and Lucent Bell Laboratories. The workstation we used for the second set of traces (SGI with one 134 MHz MIPS R4600 processor and 64 Mbytes RAM) was connected to AT&T Bell Laboratories' internal network while the workstation for the third set of traces (SGI with one 100 MHz MIPS R4000 processor and 64 Mbytes RAM) was connected to an Ethernet segment outside AT&T's rewall. During some trace collection periods all TCP connections (including all World Wide Web access) to the external Internet passed this Ethernet segment. We refer to the second set of traces as the internal AT&T traces and to the third set as the external AT&T traces.



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However, applications on both client and server can still fail because of other errors caused by system failures or internal problems We ll now go on from looking at a client system as a nonactive participant in a server-side availability plan to an examination of applications that need to be made highly available..

Fig. 2.4 Logscale diagrams with biscaling. Internet UDP packet data displaying two scaling regimes, examples of biscaling. Left: Delay series. Regime I on the left (small j's) is related to continuous but nondifferentiable sample paths h P 0; 1 and regime II (large j's) to longrange dependence. Right: Loss series (1 for lost packets, else 0). Regime I corresponds to discontinuous sample paths h < 0 . In regime II there is trivial white noise scaling a 0 , indicating stationary SRD behavior.





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Even though client systems may not be highly available, users will need some way to access the well-conditioned services you ve built. Make sure there are client systems that are physically and logically available to users. The widespread use of PCs with large internal disks poses a risk to valued data loss. Get critical data onto servers where you can employ the backup schemes that form the base of the Availability Index. Client systems should not normally be affected by reconfiguration or recovery of highly available server systems; however, there may be client application effects that need to be handled as transparently as possible.

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the context of delays (left) and losses (right) experienced by consecutive user datagram protocol (UDP) packets sent over the Internet in a regular stream (see Andren et al. [12] for details of such data sets). In both gures the alignment at large scales corresponds to long-range dependence, whereas at small scales it is associated with highly irregular sample paths. Note that when second-order properties are insuf cient to fully describe the scaling nature of the process (an extreme example is afforded by multifractals), then the correct interpretation of each branch of the bisecting will require the examination of logscale diagrams across a range of orders, as discussed in Section 2.3.3. 2.3.2 Estimation Within the Logscale Diagram

Architects and interior designers revile and battle each other. Stewart Brand, in How Buildings Learn

In this subsection it is assumed that a scaling range j P j1 ; j2 has been correctly identi ed Sums over j, and regressions, are always taken over this range The estimators to be de ned are semiparametric, as they depend on the range of scales j P j1 ; j2 , where scaling is deemed to be present, and the scaling property P1 valid there, but not on any tightly speci ed parametric model 2321 Estimating the Scaling Exponent a Because of property P1, the measurement of a is reduced to the determination of the slope over the alignment region in the logscale diagram A natural way to achieve this in a statistical estimation context is through linear regression, the de ning hypothesis of which is Eyj a j a, where a is a real constant.

Stewart Brand s book is a wonderful resource for all architects, whether you re working with silicon, steel, or sofas. The preceding quote captures the tension between those who design structures, like availability platforms, and those who make things happen within them, namely those who design the applications that run on those platforms. We ve been describing availability building blocks in series of more complex layers, starting with a foundation of data management and working up through network redundancy. Applications fit in at Level 7 of the Availability Index because they need to be well conditioned before you think about clustering them or adding other external recovery mechanisms around them. Systems and applications compose an application platform; that is, they are the pieces you use to support an application that actually does something interesting. Unless you run benchmarks for a living, you probably don t just run a database; you run an application that talks to the database.

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