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

任何计算或网络系统的工作负载中的时间相关性都被广泛认为是影响性能的重要因素，更具体地说，突发性作为时间相关性的一种形式，对性能是灾难性的。实验表明，单个服务器系统中到达强度或服务需求的突发性可能会导致用户响应时间减慢几个数量级。到目前为止，还没有一个解析的排队模型来明确地捕捉突发性。所提出的研究将使用自相关来提供突发性的形式化，该自相关表征了请求流中的时间依赖结构。将设计新的分析模型来捕捉排队系统中自相关对性能的影响，并在此基础上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.