A unified approach for Multiple Multicast Tree Construction and Max-Min Fair rate allocation

R. Maniyar, P. Ghosh, Arunabha Sen
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引用次数: 4

Abstract

Multicast communication is an efficient method of data transmission and distribution among a group, especially when network resources are inadequate and needs to be shared. Fair share of network resources, such as, bandwidth, is desirable in such cases. Although there has been an intensive research effort to design protocols and construct multicast routing graphs for a single multicast group, construction of multiple multicast groups and the fair allocation of network resources remains virtually unexplored. In this paper, a unified approach for the Multiple Multicast Tree Construction and Rate Allocation (MMTCRA) problem is addressed. The MMTCRA problem has been defined as an optimization problem with an objective of finding a Max-Min Fair rate allocation among the multiple multicast groups that co-exist in the network subject to the link-capacity constraints. The problem is proved to be NP-Complete. A Mixed Integer Linear Program (MILP) is formulated to achieve the optimal solution for this problem. A heuristic is proposed to solve the MMTCRA problem in polynomial time. The quality of the heuristic is evaluated by comparing the solution with the optimal solution for several randomly generated networks. A metric for user satisfaction, USat, has been defined in the paper. Experimental results show that 81% solutions obtained from heuristic have optimal USat, 95% solutions obtained from heuristic have optimal minimum allocated rate and the standard deviation of solutions are within 10% of optimal solutions.
一种统一的多组播树构造和最大最小公平速率分配方法
多播通信是一种有效的数据传输和分发方法,特别是在网络资源不足和需要共享的情况下。在这种情况下,公平地共享网络资源(如带宽)是可取的。尽管针对单个组播组的协议设计和组播路由图的构建已经有了大量的研究工作,但对于多组播组的构建和网络资源的公平分配仍然没有深入的研究。本文提出了一种统一的多组播树构建和速率分配(MMTCRA)方法。MMTCRA问题被定义为一个优化问题,其目标是在受链路容量约束的网络中共存的多个多播组之间找到最大-最小公平速率分配。证明了该问题是np完全的。提出了一个混合整数线性规划(MILP)来求解该问题的最优解。提出了一种在多项式时间内求解MMTCRA问题的启发式算法。通过比较几个随机生成的网络的解与最优解来评价启发式算法的质量。本文定义了用户满意度的度量标准USat。实验结果表明:81%的启发式算法解具有最优USat, 95%的启发式算法解具有最优最小分配率,且解的标准差在最优解的10%以内。
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