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necessary to model this data. For the one hour long 1995 LBL-5 dataset, the distribution of HTTP interarrival times is modeled best using a lognormal distribution while the total dataset is explained best via an exponential or Weibull model. For the one hour long 1995 DEC-4 dataset the distribution of HTTP interarrival times is modeled best using the Weibull distribution. The overall distribution is also explained best by a Weibull distribution. Yet, with a shape parameter of 0.83, the distribution is relatively close to an exponential distribution, which is not too surprising given that most connections are either TELNET or FTP connections. Indeed, because more than one- fth of all TCP connections are HTTP connections, a distribution with a heavier tail than the exponential distribution appears appropriate. While most of the application-speci c Weibull models have small discrepancies, we observe that the discrepancies are quite large for some applications such as NNTP, POP or PROTO. We identi ed two major causes for such large discrepancies: , bimodality and timer-driven applications. Since neither of our models is able to t such distorted distributions, the resulting large discrepancy values are no surprise. 15.5.5 Application-Speci c Interarrival Times over Shorter Time Periods



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can be utilized on both shared-memory and message passing architectures In a sharedmemory system, the passing of a message is often implemented as a simple memory copy Even distributed shared memory can be emulated on message passing machines with an additional software layer (eg, survey by Proti et al [154]) c Parallel Random Access Machine (PRAM) PRAM (Fortune and Wyllie [68]) is a popular machine model for algorithm design and complexity analysis Essentially, the simple model assumes an ideal centralized shared-memory machine with synchronously working processors PRAMs can be further classi ed according to how one memory cell can be accessed: only exclusively by one processor or concurrently by various processors Memory access to different cells by different processors can always be performed concurrently The advantage of PRAM is its simplicity and its similarity to the sequential von Neumann model Yet, owing to the increasing gap between processing and communication speed, it has become more and more unrealistic LogP With the proposal of the LogP model, Culler et al [46, 47] recognized the fact that the widely used PRAM model is unrealistic due to its assumption of cost-free interprocessor communication, especially for distributed systems The LogP model gained its name from the parameters that are used to describe a parallel system:





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Although the traces where collected over time periods of varying length (from less than one day to three weeks), the Weibull distribution produces good models for the interarrival times of the various applications This is a rst indication that the appropriateness of the tted Weibull distribution is not adversely affected by the considered time interval Indeed, the observations regarding the goodness of the tted distribution hold true over almost all time periods that are long enough to allow suf cient observations It even holds if we select just one compute server over a one hour time period or aggregate over a number of single user machines within one hour time periods We start by considering the HTTP connection interarrival times of the external AT&T dataset from 18 November Figure 1511 shows the discrepancy measures l2 for time periods of different length For periods of one day (Fig 15.

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11(a)), one hour (Fig 1511(b)) and even 20 minutes (Fig 1511(c)), the Weibull distribution yields the best t If we isolate speci c traf c sources within our traces (eg, a compute server at AT&T), the Weibull distribution still gives the statistically best model Figure 1512(a) depicts the arrival time series of the individual sources as well as the total arrival time series for all HTTP connections of the external AT&T dataset from 16 to 31 March over some arbitrary one hour time period Figure 1512(b) and (c) show the empirical and tted cumulative interarrival time distribution of the interarrival time distribution of two speci c sources from the same data used for Fig 1512(a).

Again the t of the Weibull distribution is far superior to all the others an observation that is underlined by the discrepancy measure for other sources and time periods The time-dependent discrepancies for the interarrival times of another application, TELNET, are shown in Fig 1513 for the 29 30 June 1995 dataset Here the rst.

L: Upper bound on the latency, or delay, incurred in communicating a message from a source to a destination processor o: Overhead time during which a processor is engaged in sending or receiving a message; during this time the processor cannot perform other operations g: Gap minimal time between consecutive message transmissions or between consecutive message receptions; the reciprocal of g corresponds to the perprocessor bandwidth P: Number of processors

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Tesseract : Simple Java Optical Character Recognition - Stack Abuse
12 Aug 2019 ... For these tasks, Optical Character Recognition ( OCR ) was devised as a ... It offers an API for a bunch of languages, though we'll focus on the Tesseract Java API. ... Get occassional tutorials , guides, and reviews in your inbox.

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