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REALISTIC LARGE-SCALE ONLINE NETWORK SIMULATION

机译:现实的大型在线网络仿真

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摘要

Large-scale network simulation is an important technique for studying the dynamic behavior of networks, network protocols, and emerging classes of distributed applications (e.g. Grid, peer-to-peer, etc.) Large scale and realism are two critical requirements for network simulations of Grid application studies. Our work here extends previous efforts in three key ways. First, we study networks 100 times larger than in our previous studies (20000 routers). Second, at this scale, we study realistic network structures (100 ASs, BGP4 and OSPF routing) versus flat OSPF routing. Finally, we describe and evaluate a new profile-based load-balancing approach called hierarchical profile-based load balance. Our extensive large-scale experiments with profile-based load balance (PROF) on flat-routed (OSPF) networks show that PROF outperforms several other techniques based on topology and static application information. However, these results and those for multi-AS networks motivate our invention of a new hierarchical technique (HPROF) which clusters network nodes to achieve a desired minimum link latency (MLL), a key determinant of simulation parallelism, then applies the graph partitioner. HPROF explicitly controls the trade-off between simulation efficiency and available parallelism, producing robust and superior performance for large-scale networks, including both single-AS and multi-AS networks. HPROF can improve load imbalance by 40%, and reduce the simulation time by about 50% in our 20000 router simulations executed on 128-node clusters. The parallel efficiency achieved by these simulations is over 40%, providing substantial capabilities for simulating large networks. In summary, these advances demonstrate that realistic large-scale network simulation for networks of 20000 routers (comparable to a large Tier-1 ISP network such as AT&T) can be accomplished with our system. To demonstrate the capabilities of our simulation tool, we simulate a large-scale Denial-of-Service attack in a large-scale network with 10000 routers organized as 40 Autonomous Systems. The simulation includes over 400 live application processes, the DoS attack and application entities, and generates aggregate traffic of over 6 Gbps.
机译:大规模网络仿真是研究网络,网络协议和新兴类别的分布式应用程序(例如网格,对等网络等)的动态行为的一项重要技术。大规模和现实是网络仿真的两个关键要求网格应用研究。我们在这里的工作从三个关键方面扩展了先前的工作。首先,我们研究的网络比以前的研究(20000个路由器)大100倍。其次,以这种规模,我们研究了实际的网络结构(100个AS,BGP4和OSPF路由)与平面OSPF路由。最后,我们描述和评估一种新的基于配置文件的负载均衡方法,称为基于分层配置文件的负载均衡。我们在平面路由(OSPF)网络上使用基于配置文件的负载平衡(PROF)进行的大规模大规模实验表明,基于拓扑和静态应用程序信息,PROF优于其他几种技术。但是,这些结果以及针对多AS网络的结果促使我们发明了一种新的分层技术(HPROF),该技术对网络节点进行聚类以实现所需的最小链路等待时间(MLL)(这是模拟并行性的关键决定因素),然后应用图分区器。 HPROF明确控制了仿真效率和可用并行性之间的折衷,从而为包括单AS和多AS网络在内的大规模网络提供了强大而卓越的性能。在128个节点的集群上执行的20000个路由器仿真中,HPROF可以将负载不平衡改善40%,并将仿真时间减少约50%。这些仿真实现的并行效率超过40%,为仿真大型网络提供了强大的功能。总而言之,这些进步表明,使用我们的系统可以完成20000路由器网络(与大型Tier-1 ISP网络(例如AT&T)相对应)的现实大规模网络仿真。为了演示我们的仿真工具的功能,我们在大规模网络中模拟了大规模拒绝服务攻击,该网络具有10000个路由器,这些路由器被组织为40个自治系统。该模拟包括400多个实时应用程序流程,DoS攻击和应用程序实体,并生成6 Gbps以上的聚合流量。

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