Delay and Energy Consumption Optimization Oriented Multi-service Cloud Edge Collaborative Computing Mechanism in IoT

Authors

  • Sujie Shao State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Jiajia Tang State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Shuang Wu State Grid Ningxia Electric Power Co. Ltd., Yinchuan, Ningxia 750001, China
  • Jianong Li China Electronics Standardization Institute, Beijing 100007, China
  • Shaoyong Guo State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Feng Qi State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China and Peng Cheng Laboratory, Shenzhen, Guangdong 518000, China

DOI:

https://doi.org/10.13052/jwe1540-9589.20810

Keywords:

Cloud-edge Collaboration, Task Allocation, Genetic Algorithm.

Abstract

The rapid development of the Internet of Things has put forward higher requirements for the processing capacity of the network. The adoption of cloud edge collaboration technology can make full use of computing resources and improve the processing capacity of the network. However, in the cloud edge collaboration technology, how to design a collaborative assignment strategy among different devices to minimize the system cost is still a challenging work. In this paper, a task collaborative assignment algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Firstly, the task collaborative assignment framework of cloud edge collaboration is constructed. Secondly, the problem of task assignment strategy was transformed into a function optimization problem with the objective of minimizing the time delay and energy consumption cost. To solve this problem, a task assignment algorithm combining the improved genetic algorithm and simulated annealing algorithm was proposed, and the optimal task assignment strategy was obtained. Finally, the simulation results show that compared with the traditional cloud computing, the proposed method can improve the system efficiency by more than 25%.

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Author Biographies

Sujie Shao, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Sujie Shao received the Ph.D. degree from the Beijing University of Posts and Telecommunication, Beijing, China, in 2015. He is currently a Lecturer with the State key Laboratory of Switching and Networking Technology of Beijing University of Posts and Telecommunication. His research interests include edge computing, the Internet of Things, smart grids, and communication network management.

Jiajia Tang, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Jiajia Tang received the bachelor’s degree in Computer Science and Technology from Beijing University of Posts and Telecommunications in 2021. She is currently pursuing the master’s degree at the Department of Computing, Beijing University of Posts and Telecommunications. Her main research interests include edge computing and the Internet of Things.

Shuang Wu, State Grid Ningxia Electric Power Co. Ltd., Yinchuan, Ningxia 750001, China

Shuang Wu was born in October 1985. He received the Bachelor’s degree in Communication Engineering from Beijing University of Posts and telecommunications, Beijing, China, in 2007. From 2015 to 2020, he was a department head of State Grid Ningxia Information & Telecommunication Company. Since 2020, he has been a vice general manager of State Grid Ningxia Information & Telecommunication Company, China. His current research interests include Information and communication management Internet of Things, Smart Grid and Dispatching management.

Jianong Li, China Electronics Standardization Institute, Beijing 100007, China

Jianong Li, born on February 24, 1990, graduated from University of New SouthWales, master degree of telecommunications. Currently working as standard development engineer in China Electronics Standardization Institute. Committed to the standardization and industry research in the field of blockchain. Main author of White Paper on China Blockchain Technology and Application Development (2016) and Research Report on China Blockchain Technology and Application Development (2018), first author of Blockchain Application Use Cases (2020), lead the development of Information technology-Blockchain and distributed ledger technology-Reference architecture, the first national standard of blockchain in China. Long-term participation in international standardization organizations (ISO/TC307, IEEE, etc).

Shaoyong Guo, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Shaoyong Guo is with the department of State Key Laboratory of Networking and Switching Technology, and received Ph.D. degree at Beijing University of Posts and Telecommunication. His research interests include Blockchain Application technology, Distributed Intelligence, Edge Computing, Energy Internet, and so on. His main contribution on Industrial Internet Data Sharing theory and technology, including the Sharing Data Complex Connection Relationship Representation Model, Data Sharing Network Resource Collaborative Optimization Mechanism, Cross-Domain Data Security and Trusted Sharing Service Mechanism. He is undertaking many key research and development projects and fund projects, and contributed to a number of pioneering standards proposals in ITU-T. And the systems and devices developed by him have large-scale application. He was awarded the second prize of science and technology progress in Henan and Jiangsu province respectively, the second prize of Science and Technology Progress Award of China Communication Society, and so on.

Feng Qi, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China and Peng Cheng Laboratory, Shenzhen, Guangdong 518000, China

Feng Qi received the B.S. and the master’ degrees from Northeastern University, Shenyang, Liaoning province, China, in 1993 and 1996, respectively.

He is currently a Professor with the Beijing University of Posts and Telecommunications, Beijing, China, where he is involved in scientific research, teaching, and standardization research in information and communications. He authored more than ten ITU-T international standards and a number of industry standards. He has successively served as the Vice Chair of the ITU-T Study Group 4 and Study Group 12. His main research interests include communications software, network management, and business intelligence. Prof. Qi was the recipient of the National Science and Technology Progress Award twice.

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Published

2021-11-21

How to Cite

Shao, S., Tang, J., Wu, S., Li, J., Guo, S., & Qi, F. (2021). Delay and Energy Consumption Optimization Oriented Multi-service Cloud Edge Collaborative Computing Mechanism in IoT. Journal of Web Engineering, 20(8), 2433–2456. https://doi.org/10.13052/jwe1540-9589.20810

Issue

2021: Vol 20 Iss 8

Section

Articles