Performance analysis of edge-PLCs enabled industrial Internet of things

Peng, Yanjun; Liu, Peng; Fu, Tingting

doi:10.1007/s12083-020-00934-1

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…(4) Performance comparison between static allocation and dynamic allocation of memory resources Figure 10 compares the total memory space occupied by the actions selected by the dynamic memory resource allocation method and the space occupied by the optimal static allocation method calculated by the Markov chain method described in our previous work [7] at each time period for processing the same piece of data. Because static allocation uses an unchanged allocation method, the amount of memory used over time is constant.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…It is in continuous progress to adapt to the complexity and uncertainty of modern industrial development. Modern PLCs gradually transform into edge-PLCs as a result of combining traditional logic control with networking, data collection, and processing [6,7]. Most PLCs have work memory and load memory for storing user programs and data, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Distributed reinforcement learning-based memory allocation for edge-PLCs in industrial IoT

Peng

Liu

et al. 2022

J Cloud Comp

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The exponential device growth in industrial Internet of things (IIoT) has a noticeable impact on the volume of data generated. Edge-cloud computing cooperation has been introduced to the IIoT to lessen the computational load on cloud servers and shorten the processing time for data. General programmable logic controllers (PLCs), which have been playing important roles in industrial control systems, start to gain the ability to process a large amount of industrial data and share the workload of cloud servers. This transforms them into edge-PLCs. However, the continuous influx of multiple types of concurrent production data streams against the limited capacity of built-in memory in PLCs brings a huge challenge. Therefore, the ability to reasonably allocate memory resources in edge-PLCs to ensure data utilization and real-time processing has become one of the core means of improving the efficiency of industrial processes. In this paper, to tackle dynamic changes in arrival data rate over time at each edge-PLC, we propose to optimize memory allocation with Q-learning distributedly. The simulation experiments verify that the method can effectively reduce the data loss probability while improving the system performance.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed reinforcement learning-based memory allocation for edge-PLCs in industrial IoT

Peng

Liu

et al. 2022

J Cloud Comp

Self Cite

View full text Add to dashboard Cite

show abstract

“…Fig. 10 compares the total memory space occupied by the actions selected by the dynamic memory resource allocation method and the space occupied by the optimal static allocation method calculated by the Markov chain method described in our previous work [6] at each time period for processing the same piece of data. Because the allocation method in static allocation is unchanged, so the memory space occupied in each period is the same.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…As shown in Fig. 11, the DMRA algorithm trains the Q table and makes a strategy for the data of that day is compared with the execution of the Markov chain method [6], the execution time is reduced by about half. Because static allocation must compare all possible memory resource allocation methods, and then find the optimal solution, the program execution time is longer.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…It is in continuous progress to adapt to the complexity and uncertainty of modern industrial development. Modern PLCs have Penh, Cambodia Full list of author information is available at the end of the article combined traditional logic control with networking, data collection, and processing, which makes them gradually evolve into edge-PLCs [5] [6]. Most of PLCs are equipped with load memory and work memory to store user programs and data respectively.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Reinforcement Learning-based Memory Allocation for Edge-PLCs in Industrial IoT

Peng

Liu

et al. 2022

Preprint

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The rapid growth of the number of devices in the industrial Internet of things (IIoT) has a huge influence in the amount of data involved. In order to alleviate the computing load of cloud servers and reduce the delay of data processing, edge-cloud computing cooperation has been introduced to the IIoT. General programmable logic controllers (PLCs), which have been playing important roles in industrial control systems, start to gain the ability of processing large amount of industrial data and sharing the workload of cloud servers. This transforms them into edge-PLCs. However, continuous influx of multiple types of concurrent production data stream against the limited capacity of built-in memory in PLCs bring a huge challenge. Therefore, ability to reasonably allocate memory resources in edge-PLCs to ensure data utilization and real-time processing has become one of the core means in improving the efficiency of industrial processes. In this paper, to tackle dynamic changes of arrival data rate over time at each edge-PLC, we propose to optimize memory allocation with Q-learning distributedly. The simulation experiments verify that the method can effectively reduce the data loss probability while improve the system performance.

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A privacy-preserving resource trading scheme for Cloud Manufacturing with edge-PLCs in IIoT

Liu

et al. 2021

Journal of Systems Architecture

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Performance analysis of edge-PLCs enabled industrial Internet of things

Cited by 6 publications

References 24 publications

Distributed reinforcement learning-based memory allocation for edge-PLCs in industrial IoT

Distributed reinforcement learning-based memory allocation for edge-PLCs in industrial IoT

Distributed Reinforcement Learning-based Memory Allocation for Edge-PLCs in Industrial IoT

A privacy-preserving resource trading scheme for Cloud Manufacturing with edge-PLCs in IIoT

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