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Adaptive orchestration of resources in distributed wide area large scale infrastructures.

机译:分布式广域大型基础架构中资源的自适应编排。

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摘要

The goal of this thesis is to take a step to understanding the resource management of massively and scattered distributed systems. A framework to predict grid resources behavior and leverage the execution of long running tasks over computational grids has been developed. This framework employs statistical analysis for estimating the resource behavior and uses a divisible load approach to increase the throughput and reduce the idle time exhibited by computational resources. The proposed approach focuses on an opportunistic pull resource selection mechanism: a number of very light agents are deployed in nonintrusive way running in a user space. Initially the framework collects information on user requirements and application deployment, assigns a subset of jobs to available resources, and periodically the selected pool of resources is updated to opportunistically choose the resources that better complete the assigned jobs. The statistical analysis process evaluates in run time different probabilistic functions to determine the one that better model a resource behavior. Experimental results show a significant reduction of the application makespan along with good estimations of the resource behavior.
机译:本文的目的是迈出一步来理解大规模分散的分布式系统的资源管理。已经开发了一种框架,可以预测网格资源的行为并在计算网格上利用长期运行的任务的执行。该框架采用统计分析来估计资源行为,并使用可分负载方法来增加吞吐量并减少计算资源所表现出的空闲时间。所提出的方法侧重于机会拉动资源选择机制:以非侵入方式在用户空间中运行部署了许多非常轻便的代理。最初,该框架收集有关用户需求和应用程序部署的信息,将作业的子集分配给可用资源,然后定期更新所选资源池,以机会主义地选择能更好地完成分配的作业的资源。统计分析过程在运行时评估不同的概率函数,以确定可以更好地对资源行为进行建模的函数。实验结果表明,随着对资源行为的良好估计,应用程序的构建时间显着减少。

著录项

  • 作者单位

    University of Puerto Rico, Mayaguez (Puerto Rico).;

  • 授予单位 University of Puerto Rico, Mayaguez (Puerto Rico).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 120 p.
  • 总页数 120
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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