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.

人们普遍认为，任何计算或网络系统的工作负载中的时间相关性都是影响性能的重要因素。更具体地说，流中的自相关对性能是灾难性的。在简单的单服务器系统中，到达强度或服务需求的自相关可能导致用户响应时间慢几个数量级。在基于大小的负载平衡策略已被证明具有最佳性能的同构集群中，作业到达强度的自相关消除了传统负载平衡策略的任何性能优势。在多层系统中，如果任一层的服务进程是自相关的，则用户响应时间非常高，尽管系统中的瓶颈资源远未饱和，并且所有其他层中测量的吞吐量和利用率也是适度的，错误地表明系统可以维持更高的容量。在存储系统中，磁盘级别的到达或服务进程的自相关可能会导致用户感知的性能显著下降。该项目旨在提供一种实用的方法来表征和量化系统中自相关流对性能的影响。主要关注的是资源分配的新技术的发展，这些技术将自相关性视为任何随机过程的重要特征。自相关的在线监测为调度参数化提供了必要的信息，为自主系统的发展迈出了重要的一步。