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no human intervention is required and transparent in the sense that existing streaming sessions are not affected in any way. For simplicity, we consider only single-server failure in the rest of the chapter but the analysis can readily be extended to cover multiple-simultaneous server failures. Let N S be the number of servers in the system, U be the storage capacity of each server, and SS be the effective server transfer capacity. The effective server transfer capacity SS is the maximum data rate at which a server can transfer data to/from the network. That is, if a server has an effective transfer capacity of SS = 600Mbps, and the server is sending data at a rate of 200Mbps, then the server will only be able to receive data up to a rate of 400Mbps.



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STATE x[n], y[m],

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It is possible that a server failure may not be caused by (unrecoverable) disk failures. In this case, we may simply disconnect the disk subsystem from the failed server and connect it to the spare server. This process is simple but requires human intervention to rst identify the source of the failure, and then to migrate the disk subsystem to the spare server. It is also possible to use an electronic wiring network to connect a disk subsystem to two or more servers, including a spare server (e.g., using twin-tailed disks). The migration of disk units can then be done electronically.

Scalar state variables need not be declared, unless they are to be used in submodels (Section 3-17) or in more than one DYNAMIC program segment 3-2 Vector Operations in DYNAMIC Program Segments The Vectorizing Compiler

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of the system s performance For example, McManus and Ross [2] suggested dividing the entire video stream into xed size intervals and then transmitting each interval with a constant bit-rate to enable control of the rate adjustment frequencies Feng et al investigated smoothing algorithms to minimize the number of rate increases [3], and to minimize the number of rate changes [4] In another two studies by Feng [5, 6], the author observed that in some cases, an algorithm targeted at minimizing a certain parameter might make too aggressive prefetches or allow too large buffer residency times The author then proposed a rate-constrained smoothing algorithm [5] and a time-constrained smoothing algorithm [6] to solve these problems Chang et al suggested that transmitting at a constant rate yields lower overhead and complexity.

< php if (is_category()) { echo '<h1 class="listhead">'; _e("Category", "notesblog"); echo ' <strong>'; single_cat_title(); echo '</strong></h1>'; } if (is_tag()) { echo '<h1 class="listhead">'; _e("Tag", "notesblog"); echo ' <strong>'; single_tag_title(); echo '</strong></h1>'; } if (is_search()) { echo '<h1 class="listhead">'; _e("Your <strong>search result</strong>", "notesblog"); echo '</h1>'; } >

Therefore, they proposed a smoothing algorithm that switches a single constant transmission rate on and off to adapt to the video bit-rates [7] Salehi et al investigated the optimal smoothing algorithm [8] that produces smoothing schedules with minimum peak rates and rate variations Interested readers are referred to the excellent study by Feng and Rexford [10] for a detailed survey and comparison of various smoothing algorithms Besides smoothing algorithms based on nding a path inside the feasible region, there are also other related studies in this area For example, Zhang [9] proposed smoothing using buffers located in multiple intermediate nodes in the network Zhao and Tripathi [11] proposed an algorithm to multiplex smoothed VBR streams to further reduce bit-rate variations Liu et al.

Assume that the experiment protocol has declared vectors y1, y2, y3, all of the same dimension n, say with

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