A Particle Swarm Optimization with Imbalance Initialization and Task Rescheduling for Task Offloading in Device-Edge-Cloud Computing

Smart devices, e.g., smart-phones, internet-of-thing device, has been prevalent in our life. How to take full advantage of the limited resources to satisfy as many as requirements of users is still a challenge. Thus, in this paper, we focus on the task offloading problem to address the challenge by device-edge-cloud computing, by PSO improved with the imbalance initialization and the task scheduling. The imbalance initialization is to increase the probability that a task is assigned to a computing node such that the node provides a longer slack time. The task scheduling is to reassign tasks with deadline violations into other nodes, to improve the number of accepted tasks for each offloading solution. Extensive experiment results show that our proposed algorithm has better performance than other ten classical and up-to-data algorithms in both the maximization of the accepted task number, the resource utilization, as well as the processing rate.

@article{Fu2023,
  title     = {A Particle Swarm Optimization with Imbalance Initialization and Task Rescheduling for Task Offloading in Device-Edge-Cloud Computing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {4},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Hui Fu and Guangyuan Li and Fang Han and Bo Wang},
  doi       = {10.14569/IJACSA.2023.01404102},
  url       = {https://doi.org/10.14569/IJACSA.2023.01404102}
}