CAREER: Scalable, High Performance Network Simulations Using Reverse Computation

职业：使用反向计算进行可扩展的高性能网络模拟

• 批准号: 0133488
• 负责人: Christopher Carothers
• 金额: --
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0133488&HistoricalAwards=false
• 关键词: CAREER; Scalable; Performance; Network; Simulations

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.

互联网数据流量每年都在翻一番。如果这一速度继续下去，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没有包括网络电话和支持网络的移动PDA产生的潜在数据，也没有包括未来可能大幅增加当前带宽需求的不可预见的“杀手级应用程序”的影响。因此，总体来说，数据增长率可能要高得多，至少，一些“热门网站”每年的流量可能会翻两番。不幸的是，由于技术障碍，每根光纤的带宽增长率将被限制在每年仅翻一番。因此，可能会导致可用带宽不足，从而给超负荷工作的网络管理团队带来有效管理带宽的负担。网络管理者需要技术和工具，使他们不仅能够“现播”他们的本地网络，而且能够“现播”周围的网络，以便在面对动态的、高带宽的、下一代“杀手级网络应用”时确保稳定、有效的带宽分配。为了解决这个“现播”问题，我们提出了一种新的并行模拟建模技术，称为“反向计算”。在这里，为并行执行而设计的网络模型能够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，反向计算被证明可以将并行乐观模拟的状态存储需求减少100倍，并将总体加速比提高6倍。我们还相信，反向计算将允许大规模网络模型扩展到更大的处理器配置，并能够更有效地设计模拟实验。本项目的总体目标是了解反向计算应用于大规模系统建模时的基本功能和性能限制。由于网络模型的重要性和紧迫性，我们选择了网络模型作为我们的驱动应用程序。为了实现这一目标，我们提出了以下五个主要研究方向的研究：1.设计和实现完全可逆的计算算法，用于事件列表管理，以及时间管理，以实现可扩展的、高效的乐观事件处理；2.开发过程和技术，以在并行模拟、反向计算框架中有效地模拟大规模、多协议的网络场景；3、反向计算性能与最先进的保守同步技术的比较和对比，4.创造利用反向计算的模拟实验设计的新方法和技术，以及5.探索反向计算、量子计算和经典并行/分布式计算之间的联系，可能会导致对这些不同的计算类别有一个更统一的看法。