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Since elastic ows use bandwidth more ef ciently, blocking probability (16.2) can be considerably less than the corresponding probability for stream traf c requiring constant rate y, as given by Erlang's formula E nr; n . The line labeled ``elastic'' in Fig. 16.3 shows achievable utilization r for elastic traf c such that Be , given by Eq. (16.2), is equal to 0.01. These results clearly illustrate the scale economies effect and the greater ef ciency of elastic sharing. The advantage of elastic sharing with respect to rigid rate allocations is somewhat mitigated in a network where ows cannot always attain a full share of available link bandwidth because of congestion on other links of their path and their own limited peak rate. If, however, the ows can at least attain rate y and this rate is guaranteed by admission control on every network link, the utilization predicted by the Erlang formula constitutes a lower bound. In other words, the Erlang formula can be used as a conservative dimensioning tool to determine the traf c capacity of a link dedicated to elastic traf c: a link of capacity c can handle a volume of elastic traf c Ae ( ow arrival rate average size) with minimum throughput y and blocking probability less than E if E Ae ; c=y E. Given the scale economies achieved with the Erlang formula, this simple dimensioning approach is ef cient if c=y is large (e.g., Ae =c > 0:8 if c=y > 100 for a 1% blocking probability). An advantage of the above approach is that the integration of stream and elastic traf c is taken into account simply by including the latter as an additional traf c class in the multirate dimensioning methods alluded to in the previous section. 16.7 CONCLUSION



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Do it quickly or do it safely sounds like something the metal shop teacher drills into your head before you turn on your first power tool. The quick-orsafe trade-off also exists for database systems. The more you focus on high performance, the more you risk increasing the recovery time. Database insert and update performance comes from increasing the depth of the replay logs, and from caching more structures in memory. The deeper the logs, the longer they take to replay. The intent is to allow the system to catch up and flush out the logs once a peak in inserts or updates has passed, but if you happen to crash at the apex of the performance peak, you ll have an extra-long recovery window.





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The realization of quality of service guarantees in a multiservice network depends more on sound traf c engineering than on the de nition of a service model allowing priority access for an unde ned number of privileged users. We have argued that the service model should facilitate traf c engineering by distinguishing two broad categories of traf c: stream and elastic. For each category, the appropriate entity for traf c management is an individual ow (e.g., one videoconference, one le transfer) and not either an isolated packet or some aggregation of ows. A tentative simple service model is based on just two traf c classes. One class destined for stream traf c is based on open-loop control and uses ``bufferless multiplexing'' with measurement-based admission control. This choice enables delay and loss rate performance guarantees, even for self-similar ows. The leaky bucket is not useful as a traf c descriptor and the only traf c parameter required here is the ow peak rate. The second service class uses closed-loop control to share bandwidth between elastic ows. We advocate a lightweight form of admission control for elastic traf c, requiring that each link identify the ows it is currently transporting. Per ow queueing would be useful to enforce fairness, or to share bandwidth more ef ciently by giving priority to short transfers, for example. In the simple bandwidth sharing models considered here, the heavy-tailed distribution of the size of transferred

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Free OCR Softwares. Google's & HP's Tesseract. Google's Keep. Microsoft Document Imaging ( MODI ) ( assuming majority of us would be having a windows OS ) Microsoft One Note. Microsoft Oxford Project API ( This API is free until some time ) FreeOCR ( This is based on Tesseract engine again )

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documents does not adversely affect the response time performance of closed-loop control. We consider charging as a means to recover the network provider's costs rather than as a tool for congestion control. Prices would ideally be set to just ensure pro tability when the network is dimensioned to handle all the offered traf c with good quality of service. There appears no essential reason to price stream and elastic traf c differently per byte transported. Users would naturally choose the service class best suited to their quality of service requirements: low delay for stream ows, high throughput for elastic ows. We have given some indications of how traditional traf c engineering practice might be extended to a multiservice network based on the proposed simple service model. The basic principle is that transparency and throughput quality of service are assured by means of admission control acting at ow level, while the network is sized to produce a suf ciently low blocking probability.

follows: 1414214, 196, and 29,725,411 For the sake of completeness we offer one more example: the mean of 999, 1001, 9999, 10002, and 9999 is still 10, and the standard deviation is 0006419 As these comparisons illustrate, the closer the members of the population are to the mean, the lower becomes Or in other words, the lower is, the more indicative of final results the mean is (You can unclench your teeth now; we have finished talking about statistics) When looking at hardware components, therefore, you ll want MTBF figures that are associated with low standard deviations But good luck in obtaining MTBF numbers; most hardware vendors don t like sharing such data If you can get these numbers, however, they will tell you a lot about the quality of the vendor s hardware.

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