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Thực hiện tiếng nói qua IP (P2)
TECHNOLOGIES SUPPORTING VoIP1 In this chapter, we discuss and review various standard and emerging coding, packetization, and transmission technologies that are needed to support voice transmission using the IP technologies. Limitations of the current technologies and some possible extensions or modifications to support high-quality—that is, near-PSTN grade—real-time voice communications services using IP are then presented. VOICE SIGNAL PROCESSING For traditional telephony or voice communications services, the base-band signal between 0.3 and 3.4 KHz is considered the telephone-band voice or speech signal. This band exhibits a wide dynamic amplitude range of at least 40 dB....
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Thực hiện tiếng nói qua IP (P3)
EVOLUTION OF VoIP SIGNALING PROTOCOLS1 This chapter reviews the existing and emerging VoIP signaling and call control protocols. In PSTN networks, ISUP (ISDN user part) and TCAP (transaction capabilities application part) messages of the SS7 protocol [1] are commonly used for call control and interworking of services. The first generation (released in 1996) of VoIP signaling and media control protocols, such as ITU-T’s H.225/H.245—defined under ITU-T’s H.323 umbrella protocol [2]—was intended to o¤er LAN-based real-time VoIP services....
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Thực hiện tiếng nói qua IP (P4)
CRITERIA FOR EVALUATING VoIP SERVICE1 In this chapter, I describe a set of important criteria that can be used to perform qualitative and quantitative measurements of IP phone or POTS phone (black phone) to black phone/IP phone voice calls over an IP network. Since a legacy POTS call, with all of its robust characteristics over an IP network, is considered to be a killer application (service) by many of the proponents of VoIP, it is recommended that a private IP network or Intranet be used for measuring performance....
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Thực hiện tiếng nói qua IP (P5)
A TESTBED FOR EVALUATING VoIP SERVICE1 A new service must be prototyped and tested in a laboratory environment before massive deployment. This allows objective and subjective evaluation of the service in question. In addition, the findings can be used for tuning the network operations and performance control parameters, as required for maintaining acceptable QoS (as discussed in Chapter 4). The testbed presented in this chapter consists of a variety of PSTN and IP domain network elements [1]. These elements are required to emulate PSTN and IP networks, IP network impairments, and elements of SS7 networks like SCP and STP....
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Thực hiện tiếng nói qua IP (P6)
VoIP DEPLOYMENT IN ENTERPRISES1 Enterprises are probably the first ones to derive the benefits of running realtime telephony and associated services over an IP network. Before the advent of VoIP, enterprises generally had phone lines for real-time voice and fax services, and a data network based on dial-up, X.25, frame relay (FR), ATM, IP, and so on for data communications services [1]. IEEE standard 802.3 protocol or Ethernet-based LANs are very common in enterprises [2] for data communications networking. ...
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Thực hiện tiếng nói qua IP (P7)
VoIP IN THE PUBLIC NETWORKS1 VoIP technology is currently mature enough to be implemented in public networks (PSTN, cable TV [CATV], etc.), at least for long-distance telecommunications services to both residential and corporate customers. Either a private IP-based network (an Intranet) or an IP-based VPN can be used to guarantee the required QoS (call acceptance/drop rate, voice quality, etc.). In order to launch VoIP in the access loop, IP-based local access over digital subscriber line (DSL) or Ethernet in the first mile (EFM, IEEE P802.3ah) access, CATV networks, and wireless local loop (WLL) can be utilized....
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Thực hiện tiếng nói qua IP (P8)
VoIP FOR GLOBAL COMMUNICATIONS1 This Chapter discusses how IP-based voice communications can be deployed for global communications in multinational enterprises and for international calling by residential PSTN customers. In traditional PSTN networks, various countries use their own version of the ITU-T standards for signaling or for bearer or information transmission. When IP-based networks, protocols, interfaces, and terminals (PCs, IP phones, Web clients, etc.) are used, unification of transmission, signaling, management, and interfaces can be easily achieved....
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Thực hiện tiếng nói qua IP (P9)
CONCLUSIONS AND CHALLENGES1 The technologies and standards required to implement VoIP service are currently well established and mature. Many equipment manufacturers are presently marketing interoperable products—such as IP phones, call servers or managers, MGWs and SGs, and so on—to realize and manage the VoIP service. Advances in digital signal processing and networking software, hardware, and protocol technologies—as discussed in Chapters 2 and 3—have stimulated the development of plug-and-talk-based IP phones. These phones can support VoIP service over IP-based networks equipped with a call controller (CC) and MGW. ...
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Grid Computing P1
The Grid is the computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science and entertainment [1–5]. Grids, illustrated in Figure 1.1, integrate networking, communication, computation and information to provide a virtual platform for computation and data management in the same way that the Internet integrates resources to form a virtual platform for information.
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Grid Computing P2
As computer networks become cheaper and more powerful, a new computing paradigm is poised to transform the practice of science and engineering. Driven by increasingly complex problems and propelled by increasingly powerful technology, today’s science is as much based on computation, data analysis, and collaboration as on the efforts of individual experimentalists and theorists. But even as computer power, data storage, and communication continue to improve exponentially, computational resources are failing to keep up with what scientists demand of them....
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Grid Computing P3
The last decade has seen a substantial change in the way we perceive and use computing resources and services. A decade ago, it was normal to expect one’s computing needs to be serviced by localised computing platforms and infrastructures. This situation has changed; the change has been caused by, among other factors, the take-up of commodity computer and network components, the result of faster and more capable hardware and increasingly sophisticated software.
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Grid Computing P4
Recent developments in high-performance networks, computers, information servers, and display technologies make it feasible to design network-enabled tools that incorporate remote compute and information resources into local computational environments and collaborative environments that link people, computers, and databases into collaborative sessions.
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Grid Computing P5
Over the past several years there have been a number of projects aimed at building ‘production’ Grids. These Grids are intended to provide identified user communities with a rich, stable, and standard distributed computing environment. By ‘standard’ and ‘Grids’, we specifically mean Grids based on the common practice and standards coming out of the Global Grid Forum (GGF) (www.gridforum.org).
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Grid Computing P6
The term ‘the Grid’ was coined in the mid-1990s to denote a proposed distributed computing infrastructure for advanced science and engineering [1]. Considerable progress has since been made on the construction of such an infrastructure (e.g., [2–5]), but the term ‘Grid’ has also been conflated, at least in
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Grid Computing P7
This chapter presents aspects of the UK e-Science communities’ plans for generic Grid middleware. In particular, it derives from the discussions of the UK Architecture Task Force [1]. The UK e-Science Core Programme will focus on architecture and middleware development in order to contribute significantly to the emerging Open Grid Services Architecture (OGSA) [2]. This architecture views Grid technology as a generic integration mechanism assembled from Grid Services (GS), which are an extension of Web Services (WS) to comply with additional Grid requirements. ...
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Grid Computing P8
Until recently, application developers could often assume a target environment that was (to a useful extent) homogeneous, reliable, secure, and centrally managed. Increasingly, however, computing is concerned with collaboration, data sharing, and other new modes of interaction that involve distributed resources. The result is an increased focus on the interconnection of systems both within and across enterprises, whether in the form of intelligent networks, switching devices,
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Grid Computing P9
A Grid can be defined as a layer of networked services that allow users single sign-on access to a distributed collection of compute, data, and application resources. The Grid services allow the entire collection to be seen as a seamless information processing system that the user can access from any location. Unfortunately, for application developers, this Grid vision has been a rather elusive goal. The problem is that while there are several good frameworks for Grid architectures (Globus [1] and Legion/Avaki [18]), the task of application development and deployment has not become easier....
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Grid Computing P10
In 1994, we outlined our vision for wide-area distributed computing [1]: For over thirty years science fiction writers have spun yarns featuring worldwide networks of interconnected computers that behave as a single entity. Until recently such science fiction fantasies have been just that. Technological changes are now occurring which may expand computational power in the same way that the invention of desktop calculators and personal computers did
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Grid Computing P11
Since the early days of mankind the primary motivation for the establishment of communities has been the idea that by being part of an organized group the capabilities of an individual are improved. The great progress in the area of intercomputer communication led to the development of means by which stand-alone processing subsystems can be integrated into multicomputer communities. – Miron Livny, Study of Load Balancing Algorithms for Decentralized Distributed Processing Systems, Ph.D. thesis, July 1983. Ready access to large amounts of computing power has been a persistent goal of computer scientists for decades....
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Grid Computing P12
For over four years, the largest computing systems in the world have been based on ‘distributed computing’, the assembly of large numbers of PCs over the Internet. These ‘Grid’ systems sustain multiple teraflops continuously by aggregating hundreds of thousands to millions of machines, and demonstrate the utility of such resources for solving a surprisingly wide range of large-scale computational problems in data mining, molecular interaction, financial modeling, and so on.
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Grid Computing P13
The goal of autonomic computing is the reduction of complexity in the management of large computing systems. The evolution of computing systems faces a continuous growth in the number of degrees of freedom the system must manage in order to be efficient. Two major factors contribute to the increase in the number of degrees of freedom: Historically, computing elements, such as CPU, memory, disks, network and so on, have nonuniform advancement. The disparity between the capabilities/speeds of various elements opens up a number of different strategies for a task depending upon the environment....
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Grid Computing P14
This chapter examines how databases can be integrated into the Grid [1]. Almost all early Grid applications are file-based, and so, to date, there has been relatively little effort applied to integrating databases into the Grid. However, if the Grid is to support a wider range of applications, both scientific and otherwise, then database integration into the Grid will become important. For example, many applications in the life and earth sciences and many business applications are heavily dependent on databases. The core of this chapter considers how databases can be integrated into the Grid so that applications can access data...
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Grid Computing P15
Data Grids address computational and data intensive applications that combine very large datasets and a wide geographical distribution of users and resources [1, 2]. In addition to computing resource scheduling, Data Grids address the problems of storage and data management, network-intensive data transfers and data access optimization, while maintaining high reliability and availability of the data (see References [2, 3] and references therein). The Open Grid Services Architecture (OGSA) [1, 4] builds upon the anatomy of the Grid [5], where the authors present an open Grid Architecture, and define the technologies and infrastructure of the Grid as ‘supporting the sharing...
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Grid Computing P16
The management of data within Grids is a challenging problem. It requires providing easy access to distributed, heterogeneous data that may reside in different ‘administrative domains’ and may be represented by heterogeneous data formats, and/or have different semantic meaning. Since applications may access data from a variety of storage repositories, for example, file systems, database systems, Web sites, document management systems, scientific databases, and so on, there is a need to define a higher-level abstraction for data organization. This is generally referred to as a data collection. A data collection contains named entities that may in actuality be stored in...
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Grid Computing P17
Scientific research and development has always involved large numbers of people, with different types and levels of expertise, working in a variety of roles, both separately and together, making use of and extending the body of knowledge. In recent years, however, there have been a number of important changes in the nature and the process of research. In particular, there is an increased emphasis on collaboration between large teams, an increased use of advanced information processing techniques, and an increased need to share results and observations between participants who are not physically co-located....
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Grid Computing P18
There are no crisp definitions of Grids [1, 2] and Peer-to-Peer (P2P) Networks [3] that allow us to unambiguously discuss their differences and similarities and what it means to integrate them. However, these two concepts conjure up stereotype images that can be compared. Taking ‘extreme’ cases, Grids are exemplified by the infrastructure used to allow seamless access to supercomputers and their datasets. P2P technology is exemplified by Napster and Gnutella, which can enable ad hoc communities of low-end clients to advertise and access the files on the communal computers....
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Grid Computing P19
The fundamental value proposition of computer systems has long been their potential to automate well-defined repetitive tasks. With the advent of distributed computing, the Internet and World Wide Web (WWW) technologies in particular, the focus has been broadened. Increasingly, computer systems are seen as enabling tools for effective long distance communication and collaboration. Colleagues (and programs) with shared interests can work better together, with less respect paid to the physical location of themselves and the required devices and machinery....
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Grid Computing P20
This short chapter summarizes the current status of Grid Computational and Programming environments. It puts the corresponding section of this book in context and integrates a survey of a set of 28 chapters gathered together by the Grid Computing Environment (GCE) group of the Global Grid Forum, which is being published in 2002 as a special issue of Concurrency and Computation: Practice and Experience. Several of the chapters here are extensions or reprints of those papers.
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Grid Computing P21
The main goal of Grid programming is the study of programming models, tools, and methods that support the effective development of portable and high-performance algorithms and applications on Grid environments. Grid programming will require capabilities and properties beyond that of simple sequential programming or even parallel and distributed programming. Besides orchestrating simple operations over private data structures, or orchestrating multiple operations over shared or distributed data structures, a Grid programmer will have to manage a computation in an environment that is typically open-ended, heterogeneous, and dynamic in composition with a deepening memory and bandwidth/latency hierarchy. ...
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Grid Computing P22
The peer-to-peer (P2P) style interaction [1] model facilitates sophisticated resource sharing environments between ‘consenting’ peers over the ‘edges’ of the Internet; the ‘disruptive’ [2] impact of which has resulted in a slew of powerful applications built around this model. Resources shared could be anything – from CPU cycles, exemplified by SETI@home (extraterrestrial life) [3] and Folding@home (protein folding) [4] to files (Napster and Gnutella [5]). Resources in the form of direct human presence include collaborative systems (Groove [6]) and Instant Messengers (Jabber [7])....
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