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In this chapter, we investigate a concurrent-push parallel server architecture for designing scalable media streaming systems such as video-on-demand (VoD) systems. We employ server striping to achieve load sharing across multiple servers without requiring the additional storage overhead incurred in replication. Furthermore, by striping using a small unit size, the system is insensitive to skewness in the videos popularities. This architecture allows one to incrementally scale up the system capacity to more concurrent users by adding (rather than replacing) more servers and redistributing (rather than duplicating) video data among them. In the following, we present and analyze quantitatively the concurrent-push scheduling algorithm for scheduling disk retrieval and network transmission in parallel video servers. We show that a simple extension of the server-push service model for parallel video servers does not perform well and introduce an Asynchronous Grouped Sweeping Scheme (AGSS) to improve the system performance. Next, we present a Sub-Schedule Striping (SSS) scheme to further increase the scalability of the architecture. Using numerical results with realistic assumptions, we show that the resultant architecture can be scaled up to more than ten thousand concurrent users.



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In order to provide the basis for Semantic Web Services, a fully edged framework needs to be provided: starting with a conceptual model, continuing with a formal language to provides formal syntax and semantics (based on different logics in order to provide different levels of logical expressiveness) for the conceptual model, and ending with an execution environment, that glue all the components that use the language for performing various tasks that would eventually enable automation of service In this context, this chapter gives an overview of existing approaches to Semantic Web Services and highlights their features as far as such a fully edged framework for SWS is concerned We start by introducing, in Section 102, the most important European initiative in the area of SWS the WSMO approach to SWS In Section 103, we provide an overview of OWL-S an OWL-based Web service ontology, and in Section 10.





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Client C0 Client C1 Client C2 Client C3 . . . Client CNC-1

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4 the SWSF a language and an ontology for describing services Furthermore, we look also at other approaches IRS III (in Section 105) and WSDL-S (in Section 106) that although do not aim at providing a fully edged framework for SWS, tackle some relevant aspects of SWS In Section 107, we take a closer look at the gap usually called grounding between the semantic and the syntactic descriptions of services, and identify several approaches to deal with the grounding in the context of SWS Section 108 concludes this chapter and points out perspectives for future research in the area of Semantic Web Services..

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A parallel video server is composed of multiple independent servers connected by an interconnection network (Figure 10.1). Each server has separate CPU, memory, disk storage, and network interface. This approach ensures that the scalability of the system will not be limited by resource contention. The interconnection network can be implemented using off-the-shelf packet switches like Ethernet switches or ATM switches. We denote the number of servers in the system by N S and the number of clients by NC . Hence the client server ratio, denoted by , is NC /N S . The following sections summarize the server striping algorithm, the service model, and the scheduling algorithm employed.

The principle behind the parallel video server architecture is the striping of a video stream across all servers in the system. A server s storage space is divided into xed-size stripe units of Q bytes each. Each video title is then striped into blocks of Q bytes and stored into the servers in a round-robin manner as shown in Figure 10.1. This xed-size block striping algorithm is called space striping, as opposed to striping in units of video frames, called time striping (cf. Section 9.3.1). Space striping signi cantly simpli es the process of striping video streams encoded using inter-frame compression algorithms (e.g., MPEG), where frame size varies considerably for different frame types. Since a stripe unit in space striping is signi cantly smaller than a video title (kilobytes versus megabytes), this enables ne-grain load sharing (as opposed to coarsegrain load sharing in data partition) among servers. Moreover, the loads are evenly distributed over all servers independent of the skewness in video retrievals.

The WSMO initiative1, part of the SDK Cluster2, is the major initiative in the area of SWS in Europe and has the aim of standardizing a unifying framework for SWS which provides support for conceptual modeling and formally representing services, as well as for automatic execution of services. In this Section we provide a general overview of the elements

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