Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tiago C. S. Xavier, Flávia Coimbra Delicato, Paulo F. Pires, Cláudio L. Amorim, Wei Li, Albert Y. Zomaya
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引用次数: 2

Abstract

In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.
通过协作和能源感知资源分配管理异构和时间敏感的物联网应用
在物联网(IoT)环境中,由于云与数据源(IoT设备)之间的距离较大,云中可用的计算资源往往无法满足时间关键型应用的延迟限制。采用边缘计算可以帮助云提供满足时间关键型应用程序需求的服务。然而,在满足物联网应用需求的同时,如何巧妙地利用资源来降低网络边缘的能耗是一个挑战。在此背景下,我们提出了一种针对物联网边缘云环境的全分布式资源分配算法,该算法(i)通过促进边缘节点之间的协作来增加基础设施资源的使用,(ii)支持应用程序的异构性和通用需求,(iii)减少应用程序延迟并提高边缘的能源效率。我们将我们的算法与非协作的垂直卸载和基于边缘协作的水平方法进行了比较。仿真结果表明,与垂直和协作方法相比,该算法能够降低49.95%的物联网应用请求端到端延迟,提高95.35%的边缘节点利用率,提高92.63%的边缘节点功耗能效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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