SDN Enhanced Resource Orchestration of Containerized Edge Applications for Industrial IoT

被引:20
|
作者
Okwuibe, Jude [1 ]
Haavisto, Juuso [2 ]
Harjula, Erkki [1 ]
Ahmad, Ijaz [3 ]
Ylianttila, Mika [1 ]
机构
[1] Univ Oulu, Ctr Wireless Commun, Oulu 90014, Finland
[2] Univ Oulu, Ctr Ubiquitous Comp, Oulu 90014, Finland
[3] VTT Tech Res Ctr Finland, Espoo 02044, Finland
来源
IEEE ACCESS | 2020年 / 8卷
基金
芬兰科学院;
关键词
Containers; Industries; Cloud computing; Software; Monitoring; Automation; Hardware; 5G; AR; containerization; cloud; Docker; edge computing; HWPC; industry; 40; InfluxDB; IoT; IIoT; latency; MongoDB; multi-access; MNO; NFV; PowerAPI; power consumption; software defined networking (SDN); VR; REAL-TIME; MOBILE; NETWORKS; CLOUD;
D O I
10.1109/ACCESS.2020.3045563
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rise of the Industrial Internet of Things (IIoT), there is an intense pressure on resource and performance optimization leveraging on existing technologies, such as Software Defined Networking (SDN), edge computing, and container orchestration. Industry 4.0 emphasizes the importance of lean and efficient operations for sustainable manufacturing. Achieving this goal would require engineers to consider all layers of the system, from hardware to software, and optimizing for resource efficiency at all levels. This emphasizes the need for container-based virtualization tools such as Docker and Kubernetes, offering Platform as a Service (PaaS), while simultaneously leveraging on edge technologies to reduce related latencies. For network management, SDN is poised to offer a cost-effective and dynamic scalability solution by customizing packet handling for various edge applications and services. In this paper, we investigate the energy and latency trade-offs involved in combining these technologies for industrial applications. As a use case, we emulate a 3D-drone-based monitoring system aimed at providing real-time visual monitoring of industrial automation. We compare a native implementation to a containerized implementation where video processing is orchestrated while streaming is handled by an external UE representing the IIoT device. We compare these two scenarios for energy utilization, latency, and responsiveness. Our test results show that only roughly 16 percent of the total power consumption happens on the mobile node when orchestrated. Virtualization adds up about 4.5 percent of the total power consumption while the latency difference between the two approaches becomes negligible after the streaming session is initialized.
引用
收藏
页码:229117 / 229131
页数:15
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