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LONG Saiqin, WANG Cong, LONG Weifan, et al., “An Efficient Task Scheduling Algorithm in the Cloud and Edge Collaborative Environment,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.223, 2022.
Citation: LONG Saiqin, WANG Cong, LONG Weifan, et al., “An Efficient Task Scheduling Algorithm in the Cloud and Edge Collaborative Environment,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.223, 2022.

An Efficient Task Scheduling Algorithm in the Cloud and Edge Collaborative Environment

doi: 10.23919/cje.2022.00.223
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  • Author Bio:

    Saiqin LONG is with the College of Information Science and Technology, Jinan University, Guangzhou, 510632, China, and also with the Hunan International Scientific and Technological Cooperation Base of Intelligent network, Key Laboratory of Hunan Province for Internet of Things and Information Security, School of Computer Science, Xiangtan University, Xiangtan, 411105, China. (Email: xxgcxyxtu@sina.com)

    Cong WANG received the B.E. degree in Computer Science and Technology from Zhengzhou University,Zhengzhou,China in 2021, and he is pursuing his M.E. degree in the School of Computer Science at Xiangtan University. His research interests include cloud computing, container scheduling, and cloud-side collaboration. He is a member of the China Computer Federation (CCF). (Email: congwcn@foxmail.com)

    Weifan LONG received the BEng degree and MEng degree in Computer Science and Technology from Xiangtan University, in 2018, 2021, respectively. He is a PhD candidate with the Academy for Engineering Technology, Fudan University. His research interests include cloud computing, game theory, reinforcement learning. He is a member of Chinese Computer Federation (CCF) (Email:)

    Haolin LIU received the B.Eng. degree in 2010 and the Ph.D. degree in 2015 from Sichuan University, Chengdu, China. He is currently an associate professor with the Key Laboratory of Hunan Province for Internet of Things and Information Security and School of Computer Science, Xiangtan University, Xiangtan, China. His research interests include mobile edge computing, wireless sensor networks, and the Internet of Things. (Email:)

    Qingyong DENG is an associate professor at the School of Computer Science and Engineering & School of Software, Guangxi Normal University, China. He received his master’s degree in Signal and Information Processing from Xiangtan University, China in 2009 and Ph.D. degree in Beijing University of Posts and Telecommunications (BUPT), China in 2019. He has published more than 30 referred journal papers in his current research interests, including IoT, compressed sensing, wireless network and smart city. He is a member of IEEE and CCF. (Email: dengqingyong@xtu.edu.cn)

    Zhetao LI is a professor in School of Computer Science, Xiangtan University. He received the B.Eng. degree in Electrical Information Engineering from Xiangtan University in 2002, the M.Eng. degree in Pattern Recognition and Intelligent System from Beihang University in 2005, and the Ph.D. degree in Computer Application Technology from Hunan University in 2010. From Dec 2013 to Dec 2014, he was a post-doc in wireless network at Stony Brook University. He is a member of IEEE and CCF. (Email: liztchina@hotmail.com)

  • Received Date: 2022-03-22
  • Accepted Date: 2022-03-22
  • Available Online: 2022-03-22
  • With the advent of the 5G era and the accelerated development of edge computing and Internet of Things technologies, the number of tasks to be processed by mobile devices continues to increase. Edge nodes become incapable of facing massive tasks due to their own limited computing capabilities, and thus the cloud and edge collaborative environment is produced. In order to complete as many tasks as possible while meeting the deadline constraints, we consider the task scheduling problem in the cloud-edge and edge-edge collaboration scenarios. As the number of tasks on edge nodes increases, the solution space becomes larger. Considering that each edge node has its own communication range, we design an edge node based clustering algorithm (ENCA), which can reduce the feasible region while dividing the edge node set. Subsequently, we transform the edge nodes inside the cluster into a bipartite graph, and then propose a task scheduling algorithm based on maximum matching (SAMM). Finally, our ENCA and SAMM are used to solve the task scheduling problem. Compared with the other benchmark algorithms, experimental results show that our algorithms increase the number of tasks which can be completed and that meet the latest deadline constraints by 32%-47.2% under high load conditions.
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