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QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks

机译:快速:无线城域网中由QoS保证的高效Cloudlet放置

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摘要

This article defines the QoS-guaranteed efficient cloudlet deployment problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users, i.e., the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deployment cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms, respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deployment cost optimization problem where all cloudlets’ computing capabilities have been given, i.e., designated capacity, an efficient heuristic algorithm is further proposed to minimize the number of cloudlets. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than $$46%$$ 46 % efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about $$50%$$ 50 % averagely, owing to the calculation processes of shortest paths between APs and the sorting processes of user access delays.
机译:本文定义了在无线城域网中由QoS保证的有效的cloudlet部署问题,其目的是最小化移动用户的平均访问延迟,即成功发送和由cloudlets服务请求时的平均延迟。同时,我们尝试优化以已部署的cloudlet总数表示的总部署成本。对于第一个目标,研究了未指定容量和约束容量的情况,我们分别设计了有效的启发式算法和聚类算法。我们证明了我们的算法比现有算法更有效。对于第二个目标,我们制定了整数线性规划,以在给定的平均访问延迟要求下,最大限度地减少使用的小云的数量。设计了一种聚类算法来保证可伸缩性。对于已经给出了所有小云的计算能力(即指定的容量)的部署成本优化问题的特殊情况,进一步提出了一种有效的启发式算法,以最小化小云的数量。我们最终通过广泛的实验仿真来评估所提出算法的性能。仿真结果表明,所提出的算法在平均小云访问延迟方面的效率比现有算法高出46%〜46%。与现有算法相比,由于AP之间最短路径的计算过程和用户​​访问延迟的排序过程,我们提出的聚类和启发式算法平均可减少约50%$ 50%的已部署小云数量。

著录项

  • 来源
    《Journal of supercomputing》 |2018年第8期|4037-4059|共23页
  • 作者单位

    School of Computer Science and Technology, Guangdong University of Technology (GDUT);

    School of Computer Science and Technology, Guangdong University of Technology (GDUT);

    School of Computer Science and Technology, Guangdong University of Technology (GDUT);

    School of Computer Science and Technology, Guangdong University of Technology (GDUT);

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cloudlet; Access delay; Cloud computing; Heuristic; Clustering;

    机译:Cloudlet;访问延迟;云计算;启发式;聚类;

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