Hierarchical Scheduling Mechanisms in Multi-Level Fog Computing

被引:20
|
作者
Maciel Peixoto, Maycon Leone [1 ,2 ]
Genez, Thiago A. L. [3 ]
Bittencourt, Luiz F. [2 ]
机构
[1] Fed Univ Bahia UFBA, BR-13083970 Sao Paulo, Brazil
[2] Univ Estadual Campinas, UNICAMP, Inst Comp, BR-13083970 Sao Paulo, Brazil
[3] European Bioinformat Inst, European Mol Biol Lab, Wellcome Genome Campus, Hinxton CB10 1SD, Cambs, England
基金
巴西圣保罗研究基金会;
关键词
Data centers; Scheduling algorithms; Computer architecture; Data transfer; Dispatching; Delays; Resource management; Fog computing; cloud computing; Internet of Things; allocation; MOBILE-CLOUD; INTERNET; THINGS; EDGE; IOT; SIMULATION; TOOLKIT; SYSTEMS;
D O I
10.1109/TSC.2021.3079110
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as micro data centers, cloudlets, or fog nodes. Based on this premise, this article proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application's latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Results suggest that the proposed component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11 percent compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.
引用
收藏
页码:2824 / 2837
页数:14
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