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A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing

- Li, Xiaomin, Wan, Jiafu, Dai, Hong-Ning, Imran, Muhammad, Xia, Min, Celesti, Antonio

  • Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
  • Full Text: false
  • Reviewed:
  • Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.

Adaptive transmission optimization in SDN-based industrial internet of things with edge computing

- Li, Xiaomin, Li, Di, Wan, Jiafu, Liu, Chengliang, Imran, Muhammad

  • Authors: Li, Xiaomin , Li, Di , Wan, Jiafu , Liu, Chengliang , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Internet of Things Journal Vol. 5, no. 3 (2018), p. 1351-1360
  • Full Text: false
  • Reviewed:
  • Description: In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission. © 2018 IEEE.
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