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The burstiness of connection interarrival times has implications on the design and evaluation of algorithms such as signaling and dynamic routing as well as resource allocation for Web servers. Signaling Signaling consists of two subproblems: connection-admission control and routing. Connection-admission control decides which connections to accept or reject while routing chooses along which (multihop) path through the network to reserve resources. The connection arrival process affects not only the CPU resources necessary to perform the algorithms, but also the network resources required to achieve a desired level of blocking. Depending on a router's CPU resources, it may not be feasible to do an implicit setup for every connection. In this case one has to nd the most bene cial subset and, in addition, a different way of grouping the packets [18]. While different groupings may reduce the burstiness, they do not eliminate it.



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In the previous section we used the following Java code to copy the contents of an array:





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Hi guys, so I have been given a project to do that uses OCR to read some text from images. I've never used an OCR library so this is something very new to me.

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Java Code Examples for com. google . api .services.vision.v1.Vision .... Project: OCR -libraries File: GoogleDetection. java View source code, 5 votes, vote down ...

Connection-level simulations [14] show that an increase in burstiness of the connection arrival stream increases the level of blocking in connection-admission control. This can be even more severe for multihop paths if several links on the path are affected. Dynamic Routing Currently, routing in the Internet is completely static. Dynamic routing (e.g., on a per connection level) is a candidate for taking advantage of shifts in the traf c matrix to improve traf c engineering. Yet, if arrivals are too bursty, this may not lead to much improvement [40]. Benchmarking of Web Servers Web servers often allocate resources, such as processes, on a per TCP connection basis. SURGE [3] is a realistic Web workload generation tool that mimics a set of real users accessing a Web server. It creates a bursty connection arrival process, which exercises the resource allocation policies of Web servers. In this way it is able to detect signi cant performance problems that would have gone unnoticed using other benchmarks that do not generate a bursty arrival process.

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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.

This de nition of granularity is called weak granularity because g(G) gweak (G), (448)

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.

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

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