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There are several natural avenues for extending this result. In Willekens and Teugels [58] and Abate et al. [1] asymptotic expansion re nements to Theorem 10.3.1 were investigated. For extensions of Theorem 10.3.1 to Markov-modulated M =G=1 queues see Asmussen et al. [4], and to Markov-modulated G=G=1 queues (equivalently random walks) see Jelenkovic and Lazar [36]. Further extension of these results to more general arrival processes was obtained in Asmussen et al. [6]. Recently, Asmussen et al. [5] established an asymptotic relationship between the number of customers in a GI =GI =1 queue and their waiting time distribution. In the rest of this section recent results are presented on a GI =GI =1 queue with a nite buffer and truncated heavy-tailed arrival sequences.



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Proliferation of data on the desktop puts you at risk for data loss. Files and databases contained inside the safe glassy confines of the data center are protected by the data management design rules we ve laid down already, but you can t protect what you can t see. PCs with large internal disks have turned into one of the largest potential single points of failure for data loss in the enterprise. Loss doesn t have to mean that the disk fails in a small whirlwind of smoke; failure in the desktop sense includes data inaccessibility because your user dropped her laptop, or spilled coffee into it, or suffered laptop theft where the data goes with the rest of the hardware.





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In engineering network switches it is very common to design them as loss systems. The main performance measures for these systems are loss probabilities and loss rates. Unfortunately, there are no asymptotic results in literature that address this problem under the assumption of long-tailed arrivals. Recently, I investigated this problem [31, 33]. Here, in Theorem 10.3.2, I present the main result from my earlier work [31]. The theorem gives an explicit asymptotic characterization of the loss rate in a nite buffer queue with long-tailed arrivals. This result, in combination with results from Jelenkovic and Lazar [35], yields a straightforward asymptotic formula for the loss rate in a uid queue with long-tailed M =G=I arrivals (for more details see Jelenkovic [31, 33]). In addition, I [31, 33] derived an explicit asymptotic approximation of buffer occupancy probabilities. This approximation is uniformly accurate for buffer sizes that are away from the buffer boundaries (zero and the maximum buffer size). Furthermore, as the maximum buffer size increases, the length of the buffer around the boundaries where the approximation does not apply stays constant. This precise knowledge of the buffer probabilities allows computation of various other functionals of the nite buffer queue. The evolution of a nite buffer queue is de ned with the following recursion: QB min QB An 1 Cn 1 ; B ; n 1 n n ! 0;

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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 )

There are not many elegant solutions to the problem The following are some ideas to reduce the number of potential failures due to the loss of PC data: Synchronize to a network folder In its simplest form, you create a folder on a networked drive, one per user, that acts as the backing store for a briefcase of documents that users take with them Your goal is to treat the PC as a cache for the networked fileserver, so data loss from the cache is handled by reloading it from the network folder There is a fair bit of user behavior to dictate in this scenario, but it s the easiest approach Rely on your normal backup and restore procedures to manage the network folder, and you ve taken a giant step to ensuring the long-term survival of PC data.

where B is the buffer size. We assume that the queueing process is in its stationary regime. The loss rate is de ned as lB E QB An 1 Cn 1 B : n loss Theorem 10.3.2. EA < EC, then Let G1 be the integrated tail distribution of A. If G1 P s and lB E A B 1 o 1 loss

You may be able to use network synchronization features of Windows, or the briefcase local copy model, to automate this process as much as possible Similarly, there s no requirement that the network folder be hosted on a PC server; you can host it on any server that speaks the native file-sharing protocols used by your clients Make a network folder the default for file locations A variation on the preceding scheme is to ensure that all of your user defaults for file locations, in all personal productivity applications, default to a network drive This makes disconnected operation more difficult, so users who are frequently mobile are going to create local copies of documents However, the extra work required in locating the local folder should make the users more sensitive to copying that data off of their personal machines as soon as they re near an office again.

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