关于P2P网络数据驱动容量的注记
A note on the data-driven capacity of P2P networks
摘要：
本文研究了P2P网络中的两个容量问题。在第一种情况下，节点有无限数量的数据要发送，目标是最优地分配它们的上行链路带宽，使得每个对等体在接收数据率方面的需求得到满足。我们解决这个问题，通过一个映射从一个节点加权图具有两个标签每个节点上的边加权二分图的最大流问题。在所考虑的第二个问题中，资源分配由对等体有兴趣共享的数据资源的可用性驱动。也就是说，节点不能分配其上行链路资源，除非其具有要首先发送的数据。上行链路带宽分配的问题，然后相当于构建一组有向树的覆盖，使接收数据的节点的数量最大化，而同行的上行链路容量不被超过。我们表明，该问题是NP-完全的，并提供了一个线性规划分解解耦成一个主问题和多个从属子问题，可以解决在多项式时间。我们还设计了一个启发式算法，以便在合理的时间内计算出一个次优解。该算法只需要节点的局部知识，因此它应该支持分布式实现。我们分析了这两个问题，通过一系列的模拟实验，具有不同的网络规模和网络密度。在大型网络上，我们比较我们的启发式和它的变种与遗传算法，并表明我们的启发式计算更好的资源分配。在较小的网络上，我们对比这些性能的确切算法，并表明，资源分配履行了很大一部分的同行可以找到，即使是硬配置，没有资源过剩。
英文摘要：
We consider two capacity problems in P2P networks. In the first one, the nodes have an infinite amount of data to send and the goal is to optimally allocate their uplink bandwidths such that the demands of every peer in terms of receiving data rate are met. We solve this problem through a mapping from a node-weighted graph featuring two labels per node to a max flow problem on an edge-weighted bipartite graph. In the second problem under consideration, the resource allocation is driven by the availability of the data resource that the peers are interested in sharing. That is a node cannot allocate its uplink resources unless it has data to transmit first. The problem of uplink bandwidth allocation is then equivalent to constructing a set of directed trees in the overlay such that the number of nodes receiving the data is maximized while the uplink capacities of the peers are not exceeded. We show that the problem is NP-complete, and provide a linear programming decomposition decoupling it into a master problem and multiple slave subproblems that can be resolved in polynomial time. We also design a heuristic algorithm in order to compute a suboptimal solution in a reasonable time. This algorithm requires only a local knowledge from nodes, so it should support distributed implementations. We analyze both problems through a series of simulation experiments featuring different network sizes and network densities. On large networks, we compare our heuristic and its variants with a genetic algorithm and show that our heuristic computes the better resource allocation. On smaller networks, we contrast these performances to that of the exact algorithm and show that resource allocation fulfilling a large part of the peer can be found, even for hard configuration where no resources are in excess.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
