11 research outputs found
DOWNSTREAM RESOURCE ALLOCATION IN DOCSIS 3.0 CHANNEL BONDED NETWORKS
Modern broadband internet access cable systems follow the Data Over Cable System Interface Specification (DOCSIS) for data transfer between the individual cable modem (CM) and the Internet. The newest version of DOCSIS, version 3.0, provides an abstraction referred to as bonding groups to help manage bandwidth and to increase bandwidth to each user beyond that available within a single 6MHz. television channel. Channel bonding allows more than one channel to be used by a CM to provide a virtual channel of much greater bandwidth. This combining of channels into bonding groups, especially when channels overlap between more than one bonding group, complicates the resource allocation problem within these networks. The goal of resource allocation in this research is twofold, to provide for fairness among users while at the same time making maximum possible utilization of the available system bandwidth. The problem of resource allocation in computer networks has been widely studied by the academic community. Past work has studied resource allocation in many network types, however application in a DOCSIS channel bonded network has not been explored. This research begins by first developing a definition of fairness in a channel bonded system. After providing a theoretical definition of fairness we implement simulations of different scheduling disciplines and evaluate their performance against this theoretical ideal. The complexity caused by overlapped channels requires even the simplest scheduling algorithms to be modified to work correctly. We then develop an algorithm to maximize the use of the available system bandwidth. The approach involves using competitive analysis techniques and an online algorithm to dynamically reassign flows among the available channels. Bandwidth usage and demand requests are monitored for bandwidth that is underutilized, and demand that is unsatisfied, and real time changes are made to the flow-to-channel mappings to improve the utilization of the total available bandwidth. The contribution of this research is to provide a working definition of fairness in a channel bonded environment, the implementation of several scheduling disciplines and evaluation of their adherence to that definition, and development of an algorithm to improve overall bandwidth utilization of the system
VOIP weathermap - a VOIP QOS collection analysis and dissemination system
 Current trends point to VoIP as a cheaper and more effective long term solution than possible future PSTN upgrades. To move towards greater adoption of VoIP the future converged digital network is moving towards a service level management and control regime. To ensure that VoIP services provide an acceptable quality of service (QoS) a measurement solution would be helpful. The research outcome presented in this thesis is a new system for testing, analysing and presenting the call quality of Voice over Internet Protocol (VoIP). The system is called VoIP WeatherMap. Information about the current status of the Internet for VoIP calls is currently limited and a recognised approach to identifying the network status has not been adopted. An important consideration is the difficulty of assessing network conditions across links including network segments belonging to different telecommunication companies and Internet Service Providers. The VoIP WeatherMap includes the use of probes to simulate voice calls by implementing RTP/RTCP stacks. VoIP packets are sent from a probe to a server over the Internet. The important characteristics of VoIP calls such as delay and packet loss rate are collected by the server, analysed, stored in a database and presented through a web based interface. The collected voice call session data is analysed using the E-model algorithm described in ITU-T G.107. The VoIP WeatherMap presentation system includes a geographic display and internet connection links are coloured to represent the Quality of Service rank
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Heterogeneous Cloud Systems Based on Broadband Embedded Computing
Computing systems continue to evolve from homogeneous systems of commodity-based servers within a single data-center towards modern Cloud systems that consist of numerous data-center clusters virtualized at the infrastructure and application layers to provide scalable, cost-effective and elastic services to devices connected over the Internet. There is an emerging trend towards heterogeneous Cloud systems driven from growth in wired as well as wireless devices that incorporate the potential of millions, and soon billions, of embedded devices enabling new forms of computation and service delivery. Service providers such as broadband cable operators continue to contribute towards this expansion with growing Cloud system infrastructures combined with deployments of increasingly powerful embedded devices across broadband networks. Broadband networks enable access to service provider Cloud data-centers and the Internet from numerous devices. These include home computers, smart-phones, tablets, game-consoles, sensor-networks, and set-top box devices. With these trends in mind, I propose the concept of broadband embedded computing as the utilization of a broadband network of embedded devices for collective computation in conjunction with centralized Cloud infrastructures. I claim that this form of distributed computing results in a new class of heterogeneous Cloud systems, service delivery and application enablement. To support these claims, I present a collection of research contributions in adapting distributed software platforms that include MPI and MapReduce to support simultaneous application execution across centralized data-center blade servers and resource-constrained embedded devices. Leveraging these contributions, I develop two complete prototype system implementations to demonstrate an architecture for heterogeneous Cloud systems based on broadband embedded computing. Each system is validated by executing experiments with applications taken from bioinformatics and image processing as well as communication and computational benchmarks. This vision, however, is not without challenges. The questions on how to adapt standard distributed computing paradigms such as MPI and MapReduce for implementation on potentially resource-constrained embedded devices, and how to adapt cluster computing runtime environments to enable heterogeneous process execution across millions of devices remain open-ended. This dissertation presents methods to begin addressing these open-ended questions through the development and testing of both experimental broadband embedded computing systems and in-depth characterization of broadband network behavior. I present experimental results and comparative analysis that offer potential solutions for optimal scalability and performance for constructing broadband embedded computing systems. I also present a number of contributions enabling practical implementation of both heterogeneous Cloud systems and novel application services based on broadband embedded computing
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems