• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Black-Box Fork-Join Latency Prediction Model for Data-Intensive Applications
【24h】

A Black-Box Fork-Join Latency Prediction Model for Data-Intensive Applications

机译：数据密集型应用的黑盒式叉连接延迟预测模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The workflows of the predominant datacenter services are underlaid by various Fork-Join structures. Due to the lack of good understanding of the performance of Fork-Join structures in general, today's datacenters often operate under low resource utilization to meet stringent service level objectives (SLOs), e.g., in terms of tail and/or mean latency, for such services. Hence, to achieve high resource utilization, while meeting stringent SLOs, it is of paramount importance to be able to accurately predict the tail and/or mean latency for a broad range of Fork-Join structures of practical interests. In this article, we propose a black-box Fork-Join model that covers a wide range of Fork-Join structures for the prediction of tail and mean latency, called ForkTail and ForkMean, respectively. We derive highly computational effective, empirical expressions for tail and mean latency as functions of means and variances of task response times. Our extensive testing results based on model-based and trace-driven simulations, as well as a real-world case study in a cloud environment demonstrate that the models can consistently predict the tail and mean latency within 20 and 15 percent prediction errors at 80 and 90 percent load levels, respectively, for heavy-tailed workloads, and at any load levels for light-tailed workloads. Moreover, our sensitivity analysis demonstrates that such errors can be well compensated for with no more than 7 percent resource overprovisioning. Consequently, the proposed prediction model can be used as a powerful tool to aid the design of tail-and-mean-latency guaranteed job scheduling and resource provisioning, especially at high load, for datacenter applications.
机译：主要的数据中心服务的工作流由各种叉协议结构下划作。由于缺乏对Fork-Join结构的性能的良好了解，今天的数据中心经常在低资源利用率下运行，以满足严格的服务级别目标（SLO），例如，在尾部和/或平均延迟方面服务。因此，为了实现高资源利用率，同时满足严格的SLO，能够准确地预测用于广泛的实际兴趣的叉结构结构的尾部和/或平均延迟至关重要。在本文中，我们提出了一个黑盒式叉连接模型，涵盖了各种叉协议结构，用于预测尾部和平均延迟，分别称为叉形和叉架。我们从尾部获得高度计算有效，经验表达式，作为任务响应时间的手段和差异的函数。我们基于基于模型和追踪驱动的模拟的广泛测试结果以及云环境中的真实案例研究表明，模型可以一致地预测在80和15％的预测误差20和15％的预测误差内的尾部和平均延迟90％的负载水平分别用于重型工作负载，以及用于轻型工作负载的任何负载水平。此外，我们的敏感性分析表明，这种误差可以很好地补偿，不超过7％的资源超级设压。因此，所提出的预测模型可以用作有助于设计尾和均衡保证工作调度和资源供应的设计的强大工具，尤其是高负载，用于数据中心应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号