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PSEUDO DIAMETER - A NOVEL CONCEPT IN DESIGNING HIGHLY BANDWIDTH EFFICIENT MULTICAST ROUTING PROTOCOLSKoneru, Sindoora 01 August 2014 (has links)
Multicasting is preferred over multiple unicasts from the viewpoint of better utilization of one of the most important network resources, namely network bandwidth. Multicasting can be done in two different ways: source based tree approach and shared tree approach. This research focuses on improving bandwidth utilization of source based multicast routing protocols and also provides core selection approaches for shared tree multicasting. In this work, we have defined new concepts called pseudo diameter and super pseudo diameter by using the routing information present in Distance Vector Routing (DVR) tables. Pseudo diameter relates to the physical locations of routers and is used to control the flow of packets along the broadcast tree. Super pseudo diameter relates to the physical location of group members and is used to control the flow of packets along the multicast tree. This location aspect of routers and group members have been incorporated into the existing broadcast and multicast protocols to achieve a much improved bandwidth utilization compared to the existing approaches. These concepts have also been used in developing both static and group based core selection approaches. Pseudo diameter used in static core selection approach, and super pseudo diameter used in group based core selection approach, generates secondary and tertiary cores along with primary core to achieve fault tolerance. Besides DVR, the other widely used unicast routing protocol is the Link State Routing protocol (LSR). We have shown that a similar concept to pseudo diameter called sub diameter can be used on networks using LSR tables to achieve better bandwidth utilization in source based multicasting and in selecting a core for shared tree multicasting.
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Heterogeneous processor composition : metrics and methodsTomusk, Erik-Arne January 2016 (has links)
Heterogeneous processors intended for mobile devices are composed of a number of different CPU cores that enable the processor to optimize performance under strict power limits that vary over time. Design space exploration techniques can be used to discover a candidate set of potential cores that could be implemented on a heterogeneous processor. However, candidate sets contain far more cores than can feasibly be implemented. Heterogeneous processor composition therefore requires solutions to the selection problem and the evaluation problem. Cores must be selected from the candidate set, and these cores must be shown to be quantitatively superior to alternative selections. The qualitative criterion for a selection of cores is diversity. A diverse set of heterogeneous cores allows a processor to execute tasks with varying dynamic behaviors at a range of power and performance levels that are appropriate for conditions during runtime. This thesis presents a detailed description of the selection and evaluation problems, and establishes a theoretical framework for reasoning about the runtime behavior of power-limited, heterogeneous processors. The evaluation problem is specifically concerned with evaluating the collective attributes of selections of cores rather than evaluating the features of individual cores. A suite of metrics is defined to address the evaluation problem. The metrics quantify considerations that could otherwise only be evaluated subjectively. The selection problem is addressed with an iterative, diversity-preserving algorithm that emphasizes the flexibility available to programs at runtime. The algorithm includes facilities for guiding the selection process with information from an expert, when available. Three variations on the selection algorithm are defined. A thorough analysis of the proposed selection algorithm is presented using data from a large-scale simulation involving 33 benchmarks and 3000 core types. The three variations of the algorithm are compared to each other and to current, state-of-the-art selection techniques. The analysis serves as both an evaluation of the proposed algorithm as well as a case study of the metrics.
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