A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

[1]  Jun Tang,et al.  A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends , 2021, IEEE/CAA Journal of Automatica Sinica.

[2]  Navjeet Kaur,et al.  A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions , 2021, Concurr. Comput. Pract. Exp..

[3]  Gareth Iacobucci Government should commit to making GP premises carbon neutral by 2030, say leaders , 2021, BMJ.

[4]  Xueliang Fu,et al.  Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm , 2021, Cluster Computing.

[5]  Juan Wang,et al.  Fog Nodes Deployment Based on Space–Time Characteristics in Smart Factory , 2021, IEEE Transactions on Industrial Informatics.

[6]  Mohamed Nazih Omri,et al.  Cooperative agents-based approach for workflow scheduling on fog-cloud computing , 2021, Journal of Ambient Intelligence and Humanized Computing.

[7]  Aida A. Nasr,et al.  Reliable scheduling and load balancing for requests in cloud-fog computing , 2021, Peer-to-Peer Networking and Applications.

[8]  Licheng Jiao,et al.  A two-stage hybrid ant colony optimization for high-dimensional feature selection , 2021, Pattern Recognit..

[9]  Paul J. M. Havinga,et al.  Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification , 2021, Sensors.

[10]  Wonjae Shin,et al.  Joint Time Allocation for Wireless Energy Harvesting Decode-and-Forward Relay-Based IoT Networks With Rechargeable and Nonrechargeable Batteries , 2021, IEEE Internet of Things Journal.

[11]  Asif Ali Laghari,et al.  Review and State of Art of Fog Computing , 2021, Archives of Computational Methods in Engineering.

[12]  Mohammad Hossein Rezvani,et al.  Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms , 2021, Cluster Computing.

[13]  Jyoti Bisht,et al.  Energy Efficient and Optimized Makespan Workflow Scheduling Algorithm for Heterogeneous Resources in Fog-Cloud-Edge Collaboration , 2020, 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE).

[14]  Sumit Singh Chauhan,et al.  A review on genetic algorithm: past, present, and future , 2020, Multimedia Tools and Applications.

[15]  Abderrahim Benslimane,et al.  Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures , 2020, Comput. Networks.

[16]  Klaus Wehrle,et al.  Challenges and Opportunities in Securing the Industrial Internet of Things , 2020, IEEE Transactions on Industrial Informatics.

[17]  George Mastorakis,et al.  Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications , 2020, IEEE Transactions on Industrial Informatics.

[18]  Atta ur Rahman,et al.  Cloud of Things: architecture, applications and challenges , 2020, Journal of Ambient Intelligence and Humanized Computing.

[19]  Zheng Chang,et al.  Dynamic Resource Allocation and Computation Offloading for IoT Fog Computing System , 2020, IEEE Transactions on Industrial Informatics.

[20]  Jingxuan Huang,et al.  An Ant Colony Optimization-Based Multiobjective Service Replicas Placement Strategy for Fog Computing , 2020, IEEE Transactions on Cybernetics.

[21]  Rashid Mirzavand,et al.  A Three-Port Zero-Power RFID Sensor Architecture for IoT Applications , 2020, IEEE Access.

[22]  Rajkumar Buyya,et al.  A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment , 2020, IEEE Transactions on Services Computing.

[23]  Diptendu Sinha Roy,et al.  Multiobjective hybrid monarch butterfly optimization for imbalanced disease classification problem , 2019, International Journal of Machine Learning and Cybernetics.

[24]  Victor Chang,et al.  Scheduling Algorithms for Heterogeneous Cloud Environment: Main Resource Load Balancing Algorithm and Time Balancing Algorithm , 2019, Journal of Grid Computing.

[25]  Wei-Ho Chung,et al.  Latency-Driven Fog Cooperation Approach in Fog Radio Access Networks , 2019, IEEE Transactions on Services Computing.

[26]  Farhad Soleimanian Gharehchopogh,et al.  A comprehensive survey on symbiotic organisms search algorithms , 2019, Artificial Intelligence Review.

[27]  Juan Wang,et al.  Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing , 2019, Sensors.

[28]  Michael Kerber,et al.  Computing the Interleaving Distance is NP-Hard , 2018, Foundations of Computational Mathematics.

[29]  Krishna P. Kadiyala,et al.  All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey , 2018, J. Syst. Archit..

[30]  Lyes Khoukhi,et al.  Industrial IoT Data Scheduling Based on Hierarchical Fog Computing: A Key for Enabling Smart Factory , 2018, IEEE Transactions on Industrial Informatics.

[31]  Tiago M. Fernández-Caramés,et al.  A Fog Computing and Cloudlet Based Augmented Reality System for the Industry 4.0 Shipyard , 2018, Sensors.

[32]  Thomas Magedanz,et al.  Application of the Fog computing paradigm to Smart Factories and cyber‐physical systems , 2018, Trans. Emerg. Telecommun. Technol..

[33]  Mohammad Reza Bonyadi,et al.  A Theoretical Guideline for Designing an Effective Adaptive Particle Swarm , 2018, IEEE Transactions on Evolutionary Computation.

[34]  Hossam Faris,et al.  Improved monarch butterfly optimization for unconstrained global search and neural network training , 2018, Applied Intelligence.

[35]  Jiafu Wan,et al.  Adaptive Transmission Optimization in SDN-Based Industrial Internet of Things With Edge Computing , 2018, IEEE Internet of Things Journal.

[36]  Eryk Dutkiewicz,et al.  Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications , 2018, IEEE Transactions on Industrial Informatics.

[37]  Roch H. Glitho,et al.  A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges , 2017, IEEE Communications Surveys & Tutorials.

[38]  Zhihua Cui,et al.  Monarch butterfly optimization , 2015, Neural Computing and Applications.

[39]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[40]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[41]  Attahiru Sule Alfa,et al.  Queueing Theory for Telecommunications - Discrete Time Modelling of a Single Node System , 2010 .

[42]  B. Suman,et al.  A survey of simulated annealing as a tool for single and multiobjective optimization , 2006, J. Oper. Res. Soc..

[43]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[44]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[45]  Dilip Kumar Sharma,et al.  Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing , 2022, Comput. Syst. Sci. Eng..

[46]  Hao Ma,et al.  A Multi-Objective Task Scheduling Method for Fog Computing in Cyber-Physical-Social Services , 2020, IEEE Access.

[47]  Shudong Wang,et al.  Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing , 2020, IEEE Access.

[48]  Juan Wang,et al.  Computing modes-based task processing for industrial internet of things , 2019, Int. J. Auton. Adapt. Commun. Syst..

[49]  Xue Liu,et al.  Distributed Coordination of Internet Data Centers Under Multiregional Electricity Markets , 2012, Proceedings of the IEEE.