CSR-SMA: Autocorrelated Flows in Systems: Analytic Models and Applications

时间相关性是影响计算或网络系统性能的重要因素，突发性作为时间相关性的一种表现形式，对系统性能的影响是灾难性的。实验表明，在单个服务器系统中的到达强度或服务需求的突发性可能导致用户响应时间慢几个数量级。到今天为止，没有分析的突发性模型存在，明确捕捉burstyness.The拟议的研究将提供一个形式化的突发性使用自相关的时间依赖结构的特点，在请求流。将设计新的分析模型，捕捉自相关的性能影响，在此基础上，将开发新的资源分配和调度策略。

Temporal dependence within the workload of any computing or networking systemhas been widely recognized as a significant factor affecting performance.More specifically, burstiness, as a form of temporal dependency, is catastrophic for performance. Experiments have shown that burstiness in the arrival intensities or service demands in a single server system may result in user response times that are slower by several orders of magnitude. To this day, no analytic queueing models exist that explicitly capture burstiness.The proposed research will provide a formalization of burstiness using autocorrelation which characterizes the temporal dependence structure in request flows. New analytic models that capture the performance effects ofautocorrelation in queueing systems will be devised, and based on these, new resource allocation and scheduling policies will be developed.