Efficient algorithms for performing packet broadcasts in a mesh network

被引:14
作者
Modiano, E [1 ]
Ephremides, A [1 ]
机构
[1] UNIV MARYLAND,DEPT ELECT ENGN,COLLEGE PK,MD 20742
基金
美国国家科学基金会;
关键词
Manuscript received March 1993; revised April 1995; approved by IEEE/ACM TRANSACTIONONS NETWORKINEGd itor N. Shacham. This work was supported by The Naval Research Laboratory and by the National Science Foundation under Grant NSF-EEC94-02384. E. Modiano is with the Communications Division; MIT Lincoln Laboratory; Lexington; MA 02173 USA (email: modiano@Il.mit.edu). A. Ephremides is with the Electrical Engineering Department; University of Maryland; College Park; MD 20742 USA (email: tony@eng.umd.edu). Publisher Item Identifier S 1063-6692(96)06092-X. In the case of optical beams; each node can have individual mirrors looking at its transmitting neighbors permanently; while it must slew its transmission mirror toward each receiving neighbor separately. In this paper; for simplicity; we assume the slew delay to be negligible; however we seek algorithms which require very little slewing;
D O I
10.1109/90.532872
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We consider processors communicating over a mesh network with the objective of broadcasting information among each other, One instance of the problem involves a number of nodes all with the same message to be broadcasted. For that problem, a lower-bound on the time to complete the broadcast, and an algorithm which achieves this bound are presented. In another instance, every node in the mesh has packets to be broadcast arriving independently, according to a Poisson random process, The stability region for performing such broadcasts is characterized, and broadcast algorithms which operate efficiently within that region are presented, These algorithms involve interacting queues whose analysis is known to be very difficult, Toward that end we develop an approximation which models an n-dimensional infinite Markov chain as a single-dimensional infinite Markov chain together with an n-dimensional finite Markov chain. This approximate model can be analyzed and the results compare favorably with simulation.
引用
收藏
页码:639 / 648
页数:10
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