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Meyn, Sean P. Pandit, Charuhas Pravin 2015-09-25T20:08:38Z 2015-09-25T20:08:38Z 10000-01-01 2004 2004 The goal in the admission control problem considered here is to choose a suitable algorithm for admitting or rejecting sources on the basis of on-line measurements of packet statistics, in order to keep a certain overflow probability below a pre-specified threshold. The theory of extremal distributions developed in this thesis is applied to the design of robust algorithms for measurement-based admission control. In addition, models are developed for the evolution of flows and packets in the admission control system, and performance evaluation of the proposed algorithms is carried out through both simulations and analysis. Results show that the robust algorithms minimize the overflow probability among all moment-consistent algorithms. Made available in DSpace on 2015-09-25T20:08:38Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3153394.pdf: 5422653 bytes, checksum: 3f2f22f1d3fc9eea038ef39a7b65b627 (MD5) Previous issue date: 2004 Embargo set by: Seth Robbins for item 82165 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 104 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004. http://hdl.handle.net/2142/80883 (MiAaPQ)AAI3153394 eng Engineering, Electronics and Electrical Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.