Abstract
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.
Highlights
T HE ongoing evolution in industrial automation and connectivity for smart factories has created an avenue for harnessing new and existing technologies towards the improvement of productivity and efficiency across manufacturing and process automation
We propose that by offloading the video post-processing to the edge, we can reduce power consumption to 1/3 of what would be needed if the same workload would be done on-device
WORK In this work, we have investigated the trade-offs involved in leveraging software-defined solutions and container-based orchestration techniques in industrial automation
Summary
T HE ongoing evolution in industrial automation and connectivity for smart factories has created an avenue for harnessing new and existing technologies towards the improvement of productivity and efficiency across manufacturing and process automation. Efforts in both academia and the industry are geared towards streamlining business operations and manufacturing processes to meet the high expectations of the ongoing industrial revolution; the industry 4.0 [1]. This evolution cuts across various industry sectors including supply chain management, monitoring systems, data analytics, and various feedback loops.
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