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Parallel Multicast Overlay Networks with Probabilistic Path Selection

Johnston, David A.
http://rave.ohiolink.edu/etdc/view?acc_num=case1327321813

Abstract Details

2012, Doctor of Philosophy, Case Western Reserve University, EECS - Computer Engineering.
Smart phones, movies on demand, regulated industrial process information; the thirst for data access has never been greater and will only continue to grow. Our network infrastructure must continue to evolve to meet the ever increasing demand for data. This is especially true when many devices demand the same data at the same time. Since improvement in pure network bandwidth capabilities is only part of the solution; many researchers have investigated efficient transfer of information and a variety of solutions have been proposed. One popular solution is the multicast overlay network which can be described as a tree. Many single multicast tree solutions and multiple multicast tree solutions have been developed; however, we still need to make these solutions more efficient. This research will focus on the use of multiple multicast trees. This is often referred to as striping where one multicast tree is one stripe. Typically in these models, each multicast tree is used equally; however, not every multicast tree has the same performance. This new method, called Probabilistic Multicast Trees (PMT), will build upon other multiple multicasting models. Given a number of multicast trees from source node to destination nodes using the multiple multicast trees, a probability of usage is calculated for each of the multicast trees with the highest probability for packet transmission assigned to the most efficient tree. Feedback is generated from destinations to source to provide the input for the probability calculations. For a given packet transmission, one tree will be chosen randomly based on the tree’s collective probability distribution and the packet will be sent on this tree. The trees’ probability distributions will be calculated and continually adjusted based on feedback of each tree’s performance. Packet transmission continues with periodic adjustment to the multicast tree usage probability based on multicast tree feedback measurements. It is the use of feedback and probability adjustments that makes PMT more efficient than other methods.
Christou Papachristou, PhD (Committee Chair)
Francis Merat, PhD (Committee Member)
Michael Rabinovich, PhD (Committee Member)
Francis Wolff, PhD (Committee Member)
David McIntyre, PhD (Committee Member)
155 p.
Computer Engineering; Computer Science; Engineering
Application Multicast; Feedback; Learned Path Selection

Recommended Citations

Citations

  • Johnston, D. A. (2012). Parallel Multicast Overlay Networks with Probabilistic Path Selection [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1327321813

    APA Style (7th edition)

  • Johnston, David. Parallel Multicast Overlay Networks with Probabilistic Path Selection. 2012. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1327321813.

    MLA Style (8th edition)

  • Johnston, David. "Parallel Multicast Overlay Networks with Probabilistic Path Selection." Doctoral dissertation, Case Western Reserve University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1327321813

    Chicago Manual of Style (17th edition)

case1327321813
549
© 2012, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.