Performance analysis of edge-PLCs enabled industrial Internet of things
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Performance analysis of edge-PLCs enabled industrial Internet of things Yanjun Peng1 · Peng Liu1
· Tingting Fu1
Received: 23 November 2019 / Accepted: 14 May 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the recent advancement in Industrial Internet of Things (IIoT), general programmable logic controllers (PLCs) have been playing more and more critical roles in industrial control systems (ICSs), such as providing local data processing, decentralized control and fault diagnosis. These so called edge-PLCs, directly receive the raw data from sensors embedded in factory equipments, put them into predefined memory space and perform analysis using programs such as the ladder logic. The challenge is how to allocate blocks in the fixed-size memory to different sensors so as to match irregular data flows. In this paper, we try to conduct performance analysis of different partition instances of the memory in the edge-PLC by modeling this problem as a multiple single-server queueing systems. We assume every sensing flow is independent of each other and has its dedicated processer. Changes can be made to partition instances to adapt to the external environment, such as the rising of order numbers or product category switching. Each state of the environment is defined by the finite state Markov chain and arrival of sensing data flows follow the stationary Poisson process. The data in the queue will expire after staying in the memory for a while. The duration of availability and service is modeled as the exponential distribution. The performance measured under different system states are analyzed in the simulation. Keywords Industrial Internet of things · Edge-PLC · Performance analysis · Queuing system
1 Introduction By integrating various sensors, mobile communications, intelligent analysis and other technologies into every link of industrial production process, industrial Internet of things (IIoT) [3] has enabled industrial control systems with vast sensing and monitoring capabilities, greatly improved manufacturing efficiency and product quality while reducing resource consumptions and manufacturing costs. In the meantime, intelligence also means more sensing data, deep calculation and fast response, which pushes computationalintensive tasks close to end devices and puts heavy burden on the computation and communication requirements. This article belongs to the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong Peng Liu
[email protected] 1
Institute of Industrial Internet, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Edge computing (EC) [2, 16, 19] has emerged as a new paradigm in IIoT by placing computing services close to the physical location of either the user or the source of the data, which provides faster, more reliable services and enables the design of high-performance and adaptive system. In such case, computational tasks and
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