CPA-ACR-CSA: Effective Resource Allocation under Temporal Dependence

CPA-ACR-CSA：时间依赖性下的有效资源分配

• 批准号: 0811417
• 负责人: Evgenia Smirni
• 金额: $ 30万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811417&HistoricalAwards=false
• 关键词: CPA; ACR; CSA; Effective; Resource

Temporal dependence within the workload of any computing or networking system has been widely recognized as a significant factor affecting performance. More specifically, autocorrelation in flows, is catastrophic for performance. In a simple single server system, autocorrelation in the arrival intensities or service demands may result in user response times that are slower by several orders of magnitude. In homogeneous clusters where size-based load balancing policies have been proved optimal for performance, autocorrelation in the arrival intensities of jobs obliterates any performance benefit of traditional load balancing policies.In multi-tiered systems, if a service process of any of the tiers is autocorrelated, then user response times are very high, in spite of the fact that the bottleneck resource in the system is far from saturation and that the measured throughput and utilizations in all other tiers are also modest, falsely indicating that the system can sustain higher capacities. In storage systems, autocorrelation in the arrival or service processes at the disk level may result in significant user-perceived performance degradation.This project aims at providing a practical way to characterize and quantify the performance impacts of autocorrelated flows in systems. The main focus is on the development of new technologies for resource allocation that consider autocorrelation as an important characteristic of any stochastic process. On-line monitoring of autocorrelation provides the necessary information for scheduling parameterization, making an important step toward the development of autonomic systems.

任何计算或网络系统工作量中的时间依赖性已被广泛认为是影响性能的重要因素。 更具体地说，流中的自相关是灾难性的。在简单的单个服务器系统中，到达强度或服务需求中的自相关可能会导致用户响应时间较慢，而这些响应时间较慢。在同质群集中，基于大小的负载平衡策略是对性能最佳的最佳选择，在工作的到达强度的到达强度中，损害了传统负载平衡策略的任何绩效益处。在多层系统中，如果任何层的服务过程都非常高，那么无论用户响应时间都很高，那么该系统的效果很高。所有其他层次的利用也很小，错误地表明该系统可以维持更高的能力。在存储系统中，磁盘级别的到达或服务过程中的自相关可能会导致用户感知的性能降低。该项目旨在提供一种实用方法来表征和量化系统中自动相关流的性能影响。主要重点是开发用于资源分配的新技术，这些技术将自相关视为任何随机过程的重要特征。自相关的在线监视为调度参数化提供了必要的信息，从而迈出了自主系统开发的重要一步。