Traffic-based Load Balance for Scalable Network Emulation
Traffic-based Load Balance for Scalable Network Emulation
复制标题
用于可扩展网络仿真的基于流量的负载平衡
DOI：
10.1145/1048935.1050190
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
A. Chien
中科院分区：
文献类型：
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作者：
Xin Liu;A. Chien
负载平衡是实现大型网络仿真研究可伸缩性的关键，这对新兴的网格、对等网络以及其他分布式应用程序和中间件具有重要意义。由于网络结构的不规则性和网络流量的不可预测性，在仿真中实现负载均衡是很困难的。我们将负载均衡描述为一个图划分问题，并将经典的图划分算法应用于该问题。这种方法的主要挑战是如何从网络仿真中提取有用的信息，并以一种反映原始仿真问题中的负载平衡要求的方式将其提供给图划分算法。使用一个名为MaSSF的大型网络仿真系统，我们探索了三种划分方法，基于纯静态拓扑信息(TOP)，结合拓扑和应用程序放置信息(Place)，以及结合拓扑和应用程序配置文件数据(Profile)。这些研究表明，利用静态拓扑和应用位置信息可以实现合理的负载均衡，但基于Profile的方法进一步提高了即使是大规模网络仿真的负载均衡。在我们的实验中，与纯粹基于静态拓扑的方法相比，Profile将负载平衡提高了50%到66%，仿真时间减少了50%。
Load balance is critical to achieving scalability for large network emulation studies, which are of compelling interest for emerging Grid, Peer to Peer, and other distributed applications and middleware. Achieving load balance in emulation is difficult because of irregular network structure and unpredictable network traffic. We formulate load balance as a graph partitioning problem and apply classical graph partitioning algorithms to it. The primary challenge in this approach is how to extract useful information from the network emulation and present it to the graph partitioning algorithms in a way that reflects the load balance requirement in the original emulation problem. Using a large-scale network emulation system called MaSSF, we explore three approaches for partitioning, based on purely static topology information (TOP), combining topology and application placement information (PLACE), and combining topology and application profile data (PROFILE). These studies show that exploiting static topology and application placement information can achieve reasonable load balance, but a profile-based approach further improves load balance for even large scale network emulation. In our experiments, PROFILE improves load balance by 50% to 66% and emulation time is reduced up to 50% compared to purely static topology-based approaches.