Block scheduling is difficult to implement in P2P network since there is no central coordinator. This problem can be solved by employing network coding technique which allows intermediate nodes to perform the coding operation instead of store and forward the received data. There is a general assumption in this area of research so far that a target download rate is always attainable at every peer as long as coding operation is performed at all the nodes in the network. An interesting study is made that a maximum download rate can be attained by performing the coding operation at relatively small portion of the network. The problem of finding the minimal set of node to perform the coding operation and links to carry the coded data is called as a network code minimization problem (NCMP). It is proved to be an NP hard problem. It can be solved using genetic algorithm (GA) because GA can be used to solve the diverse NP hard problem. A new NCMP model which considers both minimize the resources needed to perform coding operation and dynamic change in network topology due to disconnection is proposed. Based on this new NCMP model, an effective and novel GA is proposed by implementing problem specific GA operators into the evolutionary process. There is an attempt to implement the different compositions and several options of GA elements which worked well in many other problems and pick the one that works best for this resource minimization problem. Our simulation results prove that the proposed system outperforms the random selection and coding at all possible node mechanisms in terms of both download time and system throughput.

中文翻译：

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基于网络代码的对等网络中基于遗传算法的资源最小化

由于没有中央协调器，块调度在 P2P 网络中难以实现。这个问题可以通过采用网络编码技术来解决，该技术允许中间节点执行编码操作，而不是存储和转发接收到的数据。到目前为止，该研究领域有一个普遍的假设，即只要在网络中的所有节点都执行编码操作，每个对等点总是可以达到目标下载速率。一项有趣的研究表明，通过在网络的相对较小部分执行编码操作可以获得最大下载速率。找到执行编码操作的最小节点集和携带编码数据的链接的问题称为网络代码最小化问题（NCMP）。证明这是一个NP难题。它可以使用遗传算法 (GA) 来解决，因为 GA 可用于解决多样化的 NP 难题。提出了一种新的NCMP模型，该模型既考虑了执行编码操作所需的资源，又考虑了由于断开连接引起的网络拓扑动态变化。基于这种新的 NCMP 模型，通过在进化过程中实施特定问题的 GA 算子，提出了一种有效且新颖的 GA。尝试实现在许多其他问题中运行良好的 GA 元素的不同组合和几个选项，并选择最适合此资源最小化问题的一个。我们的仿真结果证明，在下载时间和系统吞吐量方面，所提出的系统在所有可能的节点机制上都优于随机选择和编码。