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but the dummy variables y$ and ydot$ need not be declared as state variables 3-18 Dealing with Sampled-data Assignments, Limiters, and Switches A user-defined function involving sampled-data assignments, limiters and/or switches (Table 3-1) generates only one line of DYNAMIC-segment code, and can be thus programmed following an OUT, SAMPLE m, or step statement as

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In Part II we depart from this architecture to investigate an alternative: parallel server architectures, that offer promising solutions to the abovementioned scalability and reliability limitations In this chapter we give an overview of the issues in designing streaming systems around parallel server architectures, and present a taxonomy to classify the many possible architectural alternatives Subsequent chapters will investigate in more detail several architectures which provide different engineering tradeoffs..

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discussed in Sections 2-10 and 2-11 However, submodels can generate multiple lines that cannot be separated by OUT, SAMPLE m, or step statements in a submodel definition As a result, a submodel must generate only differentialequation-system ( analog ) code, only limiter/switch operations operating on analog variables, or only sampled-data operations Sampled-data assignments can safely include limiter/switch operations It is then, strictly speaking, incorrect to invoke the submodel defined by

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A media server is essentially a data mover, retrieving media data from the storage devices (eg, hard disks or RAID) and then sending them over the network to the clients for playback Thus, the primary challenges in a media server are capacity and reliability Despite the rapid advances in computing and network hardware, the capacity of commodity server hardware is still rather limited Considering the emergence of high-de nition television (HDTV) where a single stream can consume tens of Mbps of bandwidth, there remain considerable challenges to building high-capacity yet cost-effective media servers Server reliability, on the other hand, has been advancing at a much slower pace, partly due to the nature of the problem and the cost of many solutions Many existing media servers, such as video servers used in video-on-demand (VoD) services, are built around the single-server architecture, ie.

, with the streaming server running in an independent server machine equipped with storage devices and networking interfaces This single-server architecture is well understood and widely adopted not only in streaming servers, but also in many other types of servers, such as a web server, an FTP server, and so on In bandwidth-demanding applications such as VoD, however, the single-server architecture will quickly run into scalability and reliability limitations First, the capacity of a single server cannot be expanded inde nitely, even if striped disk array is employed If we keep adding more disks to the server, we will eventually run into other system bottlenecks, such as the I/O bus capacity, the processor s capacity, memory limit, and so on Conceivably, we can upgrade the server hardware to higher-performance hardware platforms such as massively-parallel systems [1, 2] but the costs will be substantially increased.

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to produce triangle waves and square waves in the manner of Section 2-17 Serendipitously, the resulting code usually works anyway, presumably because we are only integrating a constant input equal to either a or a

Another approach is to replicate all the data to a new server to increase the system s capacity This will double the cost of the system but nonetheless provides a linear scalability path However, in applications where the storage requirement is large, such as in movie-on-demand applications, the multiplied storage cost could become signi cant To reduce storage overhead in replication, we can selectively replicate the media objects such as videos based on their viewing popularity [3-7] The more popular videos will be replicated across more servers to increase streaming capacity while keeping fewer copies of the less popular videos to reduce storage cost This approach will be effective if the video popularity is known Otherwise the load of the servers may not be balanced, thus leading to unnecessary request blockings.

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