CAREER: Scalable, High Performance Network Simulations Using Reverse Computation

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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.