CAREER: Scalable, High Performance Network Simulations Using Reverse Computation

互联网数据流量每年翻一番。如果按照这个速度发展下去，数据流量将在2002年左右超过语音流量。然而，互联网流量增长率的估计中不包括网络电话和移动网络pda产生的潜在数据，也不包括未来不可预见的“杀手级应用程序”的影响，这些应用程序可能会大大增加当前的带宽需求。因此，总的来说，数据增长率可能会显著提高，至少，一些“热门网站”每年的流量可能会增加四倍。不幸的是，由于技术障碍，每根光纤的带宽增长将仅限于每年翻一番。因此，可能导致可用带宽不足，从而将有效管理带宽的负担放在超负荷工作的网络管理团队身上。网络管理人员将需要技术和工具，使他们不仅可以“即时播报”本地网络，还可以“即时播报”周围的网络，以便在面对动态、高带宽、下一代“杀手级web应用程序”时确保稳定、有效的带宽分配。为了解决这个“临近预报”问题，我们建议使用一种新的并行模拟建模技术，称为“反向计算”。在这里，为并行执行设计的网络模型可以在模拟时间内向前和向后执行。对于简单的网络模型，与用于支持乐观模拟中的回滚处理的经典状态保存技术相比，反向计算已被证明可以将并行乐观模拟的状态内存需求减少100倍，并将总体速度提高6倍。我们还相信，宇宙计算将允许大规模网络模型扩展到更大的处理器配置，以及实现更有效的模拟实验设计。该项目的总体目标是了解反向计算在应用于大型系统建模时的基本功能和性能限制。由于其重要性和影响，我们选择网络模型作为我们的驱动应用。为了实现这一目标，我们建议在以下五个主要研究重点领域研究逆向计算：1。为事件列表管理和时间管理设计和实现完全可逆的计算算法，以实现可扩展的、高效的乐观事件处理，2。2 .在并行仿真、反向计算框架中有效模拟大规模、多协议网络场景的过程和技术的发展；3 .逆向计算性能与最新保守同步技术的比较与对比。4 .利用逆向计算为模拟实验设计创造新的方法和技术；对反向计算、量子计算和经典并行/分布式计算之间联系的探索，可能会对这些不同类型的计算产生更统一的看法。

Internet data traffic is doubling each year. If this rate continues,data traffic will surpass voice traffic around the year 2002. However,not included in the Internet traffic growth rate estimates are thepotential data generated by web phones and mobile web-enabled PDAs,nor are the effects of future, unforeseen ``killer apps'' that maygreat increase the current demand for bandwidth. Thus, the data growthrate could be significantly higher in the aggregate and at the veryleast, some ``hot sites'' may experience a quadrupling of traffic eachyear. Unfortunately, due to technological barriers, bandwidth growthrates per fiber will be limited to only doubling peryear. Consequently, short falls in available bandwidth may result,thus placing the burden to effectively manage bandwidth on overworked,network management teams. Network managers will require techniques andtools that enable them to "nowcast" not only their local network, butsurrounding networks as well in order to ensure, stable, effectivebandwidth allocation in the face of dynamic, high-bandwidth, nextgeneration ``killer web apps''.To address this "nowcasting" problem, we propose the use of a newparallel simulation modeling technique called "reversecomputation". Here, network models designed for parallel execution areable to execute both forwards and backwards in simulated time. Forsimplistic network models, reverse computation has been shown toreduce the state memory requirements of parallel optimisticsimulations by a factor of 100 and increase the overall speedup by afactor of 6 when compared to classic state-saving techniques used tosupport rollback processing in optimistic simulations. We also believereverse computation will allow large-scale network models to scale tomuch larger processor configurations as well as enable a moreefficient design of simulation experiments.The overall goal of this project is to understand the fundamentalfunctional and performance limits of reverse computation when appliedto the modeling of large-scale systems. Because of its importance andimpact, we have selected network models as our driving application.To achieve this goal, we propose to investigate reverse computation inthe following five major research thrust areas:1. the design and implementation of perfectly reversible computationalgorithms for event-list management, and time management to enablescalable, efficient optimistic event processing,2. the development of processes and techniques to effectively model alarge-scale, multi-protocol network scenario in a parallel simulation,reverse computation framework,3. the comparison and contrast of reverse computation performance tostate-of-the-art conservative synchronization techniques,4. the creation of new methods and techniques for the design ofsimulation experiments that take advantage of reverse computation,and5. the exploration of the linkages between reverse computation,quantum computing and classic parallel/distributed computing thatcould lead to a more unified view of these disparate classes ofcomputation.