Li, K., Wang, X., He, Q., Ni, Q., Yang, M., & Dustdar, S. (2023). Computation Offloading for Tasks With Bound Constraints in Multiaccess Edge Computing. IEEE Internet of Things Journal, 10(17), 15526–15536. https://doi.org/10.1109/JIOT.2023.3264484
Multiaccess Edge Computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this paper, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.
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Project (external):
National Key Research and Development Program of China National Natural Science Foundation of China National Natural Science Foundation of China
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Project ID:
Grant 2022YFB4500800 Grant 62032013 Grant 92267206