In-Situ Resource Provisioning with Adaptive Scale-out for Regional IoT Services
In-Situ Resource Provisioning with Adaptive Scale-out for Regional IoT Services
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DOI：
10.1109/sec.2018.00022
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Yugo Nakamura;Teruhiro Mizumoto;H. Suwa;Yutaka Arakawa;Hirozumi Yamaguchi;K. Yasumoto
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文献类型：
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作者：
Yugo Nakamura;Teruhiro Mizumoto;H. Suwa;Yutaka Arakawa;Hirozumi Yamaguchi;K. Yasumoto
在部署了数十亿物联网设备的时代，边缘/雾计算范式因其减少处理延迟和通信开销的能力而备受关注。为了提高响应用户查询创建及时的地理空间信息的区域物联网服务的体验质量（QoE），重要的是基于每个服务的计算需求有效地分配足够的资源。然而，由于边缘/雾设备被假设为异构的（就其计算能力、相对于其他设备的网络性能、部署密度等而言），根据计算需求提供计算资源成为具有挑战性的约束优化问题。在本文中，我们制定了一个延迟受限的区域物联网服务提供（dcRISP）问题。dcRISP根据区域物联网服务的需求分配设备的计算资源，以最大化用户的QoE。我们还提出了dcRISP+，dcRISP的扩展，使资源选择扩展到初始区域以外，以满足日益增长的计算需求。我们提出了一个供应算法，在原位资源区域选择与自适应横向扩展和原位任务调度的禁忌搜索的基础上，解决dcRISP+问题。我们对京都的一个旅游区进行了模拟研究，其中部署了4，000台IoT设备和3种类型的IoT服务。结果表明，我们提出的算法可以获得更高的用户QoE相比，传统的资源配置算法。
In an era where billions of IoT devices have been deployed, edge/fog computing paradigms are attracting attention for their ability to reduce processing delays and communication overhead. In order to improve Quality of Experience (QoE) of regional IoT services that create timely geo-spatial information in response to users' queries, it is important to efficiently allocate sufficient resources based on the computational demand of each service. However since edge/fog devices are assumed to be heterogeneous (in terms of their computational power, network performance to other devices, deployment density, etc.), provisioning computational resources according to computational demand becomes a challenging constrained optimization problem. In this paper, we formulate a delay constrained regional IoT service provisioning (dcRISP) problem. dcRISP assigns computational resources of devices based on the demand of the regional IoT services in order to maximize users' QoE. We also present dcRISP+, an extension of dcRISP, that enables resource selection to extend beyond the initial area in order to satisfy increasing computational demands. We propose a provisioning algorithm, in-situ resource area selection with adaptive scale out and in-situ task scheduling based on a tabu search, to solve the dcRISP+ problem. We conducted a simulation study of a tourist area in Kyoto where 4,000 IoT devices and 3 types of IoT services were deployed. Results show that our proposed algorithms can obtain higher user QoE compared to conventional resource provisioning algorithms.