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Open AccessArticle

Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay

1
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
2
School of Electronic and Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
3
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 3830; https://doi.org/10.3390/s19183830
Received: 13 August 2019 / Revised: 2 September 2019 / Accepted: 2 September 2019 / Published: 4 September 2019
(This article belongs to the Special Issue Edge/Fog/Cloud Computing in the Internet of Things)
Fog computing has recently emerged as an extension of cloud computing in providing high-performance computing services for delay-sensitive Internet of Things (IoT) applications. By offloading tasks to a geographically proximal fog computing server instead of a remote cloud, the delay performance can be greatly improved. However, some IoT applications may still experience considerable delays, including queuing and computation delays, when huge amounts of tasks instantaneously feed into a resource-limited fog node. Accordingly, the cooperation among geographically close fog nodes and the cloud center is desired in fog computing with the ever-increasing computational demands from IoT applications. This paper investigates a workload allocation scheme in an IoT–fog–cloud cooperation system for reducing task service delay, aiming at satisfying as many as possible delay-sensitive IoT applications’ quality of service (QoS) requirements. To this end, we first formulate the workload allocation problem in an IoT-edge-cloud cooperation system, which suggests optimal workload allocation among local fog node, neighboring fog node, and the cloud center to minimize task service delay. Then, the stability of the IoT-fog-cloud queueing system is theoretically analyzed with Lyapunov drift plus penalty theory. Based on the analytical results, we propose a delay-aware online workload allocation and scheduling (DAOWA) algorithm to achieve the goal of reducing long-term average task serve delay. Theoretical analysis and simulations have been conducted to demonstrate the efficiency of the proposal in task serve delay reduction and IoT-fog-cloud queueing system stability. View Full-Text
Keywords: fog system; internet of things; workload allocation; task service delay; Lyapunov drift-plus-penalty fog system; internet of things; workload allocation; task service delay; Lyapunov drift-plus-penalty
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MDPI and ACS Style

Li, L.; Guo, M.; Ma, L.; Mao, H.; Guan, Q. Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay. Sensors 2019, 19, 3830. https://doi.org/10.3390/s19183830

AMA Style

Li L, Guo M, Ma L, Mao H, Guan Q. Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay. Sensors. 2019; 19(18):3830. https://doi.org/10.3390/s19183830

Chicago/Turabian Style

Li, Lei; Guo, Mian; Ma, Lihong; Mao, Huiyun; Guan, Quansheng. 2019. "Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay" Sensors 19, no. 18: 3830. https://doi.org/10.3390/s19183830

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