Resource Provisioning in the Edge for IoT Applications With Multilevel Services
Resource Provisioning in the Edge for IoT Applications With Multilevel Services
复制标题
具有多级服务的物联网应用边缘资源配置
DOI：
10.1109/jiot.2018.2875753
复制
发表时间：
2019-06
影响因子：
10.6
通讯作者：
Ma Zhan
中科院分区：
文献类型：
--
作者：
Zhang Xu;Huang Haojun;Yin Hao;Wu Dapeng Oliver;Min Geyong;Ma Zhan
随着计算密集型和延迟敏感型物联网应用的普及，物联网服务提供商（SP）开始在边缘部署微型数据中心，并将功能分流到它们。然而，越来越复杂的物联网应用需要跨地理分布的基础设施的有序服务序列来实现其功能，这对物联网SP以低成本和高效率部署应用提出了巨大的挑战。据我们所知，没有现有的工作已经研究了部署的应用程序与多级服务（称为应用程序部署与多级服务（ADMS）问题）。为了填补差距，我们制定的ADMS问题作为一个优化问题，其目的是最大限度地减少整体部署成本的延迟/计算/存储/带宽的要求和基础设施的容量限制。我们设计了一个基于工作流的启发式算法称为AMS，它可以确定有多少虚拟机（VM）应放置为每种类型的服务，以及在哪里放置它们。AMS支持服务按需真实的扩展或缩减。基于真实的网络测量数据的仿真实验表明，在用户满意度相当的情况下，AMS可以减少部署虚拟机数量28.4%，部署成本降低33.9%.
As the prevalence of computing-intensive and delay-sensitive Internet of Things (IoT) applications, IoT service providers (SP) begin to deploy micro data centers in the edge and offload functions to them. However, more and more complex IoT applications require an ordered sequence of services across geographically distributed infrastructure to fulfil their functions, which poses grand challenges for IoT SP to deploy applications with low costs and high efficiency. To the best of our knowledge, no existing works have studied the deployment for an application with multilevel services (referred to as application deployment with multilevel services (ADMS) problem). To fill in the gap, we formulate the ADMS problem as an optimization problem with the aim of minimizing the overall deployment cost under the latency/computation/storage/bandwidth requirements and the infrastructure capacity limitations. We design a workflow-based heuristic algorithm called AMS, which can determine how many virtual machines (VMs) should be placed for each type of service and where to place them. AMS supports the services to scale up or scale down on demand in real time. Simulation experiments based on real network measurement demonstrate that AMS can reduce the number of deployed VMs by 28.4% and the deployment cost by 33.9% subject to comparable satisfied user ratio.
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影响因子：
9.3
作者：
Ren, Ju;Guo, Hui;Zhang, Yaoxue
通讯作者：
Zhang, Yaoxue
DOI：
10.1145/2070562.2070585
发表时间：
2011-11
期刊：
--
影响因子：
--
作者：
Hitesh Ballani;Paolo Costa;T. Karagiannis;A. Rowstron
通讯作者：
Hitesh Ballani;Paolo Costa;T. Karagiannis;A. Rowstron
DOI：
10.1109/icdcs.2012.74
发表时间：
2012-06
期刊：
2012 IEEE 32nd International Conference on Distributed Computing Systems
影响因子：
--
作者：
Qi Zhang;Quanyan Zhu;M. Zhani;R. Boutaba
通讯作者：
Qi Zhang;Quanyan Zhu;M. Zhani;R. Boutaba
影响因子：
8.1
作者：
Michael Vögler;Johannes M. Schleicher;Christian Inzinger;S. Dustdar
通讯作者：
Michael Vögler;Johannes M. Schleicher;Christian Inzinger;S. Dustdar
影响因子：
5.2
作者：
H. Madhyastha;T. Isdal;M. Piatek;C. Dixon;T. Anderson;A. Krishnamurthy;A. Venkataramani
通讯作者：
H. Madhyastha;T. Isdal;M. Piatek;C. Dixon;T. Anderson;A. Krishnamurthy;A. Venkataramani