School of Information Science and Technology, Donghua University, Shanghai 201620, China.
Sensors (Basel). 2021 Apr 30;21(9):3135. doi: 10.3390/s21093135.
With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered to be an effective way to solve these challenges. Due to the highly dynamic mobility of mobile devices and the randomness of the work requests, the load imbalance between MEC servers will affect the performance of the entire network. In this paper, the software defined network (SDN) is applied to the task allocation in the MEC scenario of UDN, which is based on routing of corresponding information between MEC servers. Secondly, a new load balancing algorithm based on load estimation by user load prediction is proposed to solve the NP-hard problem in task offloading. Furthermore, a genetic algorithm (GA) is used to prove the effectiveness and rapidity of the algorithm. At present, if the load balancing algorithm only depends on the actual load of each MEC, it usually leads to ping-pong effect. It is worth mentioning that our method can effectively reduce the impact of ping-pong effect. In addition, this paper also discusses the subtask offloading problem of divisible tasks and the corresponding solutions. At last, simulation results demonstrate the efficiency of our method in balancing load among MEC servers and its ability to optimize systematic stability.
随着移动设备的指数级增长和计算密集型、延迟敏感型任务的出现，对数据和计算资源的巨大需求成为了一个大挑战。幸运的是，移动边缘计算（MEC）和超密集网络（UDN）的结合被认为是解决这些挑战的有效方法。由于移动设备的高度动态性和工作请求的随机性，MEC 服务器之间的负载不平衡会影响整个网络的性能。在本文中，软件定义网络（SDN）被应用于 UDN 中的 MEC 场景下的任务分配，这是基于 MEC 服务器之间的相应信息路由。其次，提出了一种基于用户负载预测的负载估计的新负载平衡算法，以解决任务卸载中的 NP 难问题。此外，还使用遗传算法（GA）来证明算法的有效性和快速性。目前，如果负载平衡算法仅依赖于每个 MEC 的实际负载，通常会导致乒乓效应。值得一提的是，我们的方法可以有效地减少乒乓效应的影响。此外，本文还讨论了可分任务的子任务卸载问题及其相应的解决方案。最后，仿真结果证明了我们的方法在平衡 MEC 服务器之间的负载以及优化系统稳定性方面的效率。
