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

CSR-SMA：系统中的自相关流：分析模型和应用

• 批准号: 0720699
• 负责人: Evgenia Smirni
• 金额: $ 20万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720699&HistoricalAwards=false
• 关键词: CSR; SMA; Autocorrelated; Flows; Systems

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.

任何计算或网络系统的工作量中的时间依赖性被广泛认为是影响性能的重要因素。更具体地说，爆发性作为时间依赖性的一种形式，是灾难性的。实验表明，到达强度或单个服务器系统中的服务需求中的爆发性可能会导致用户响应时间的速度较慢。直到今天，尚无明确捕获爆发性的分析排队模型。拟议的研究将使用自相关来形式化爆发，而自相关表征了请求流中的时间依赖性结构。将设计捕获排队系统中大小相关性能效应的新分析模型，并基于这些新的资源分配和调度策略。