Communications Non-Commissioned Officer School, Army Engineering University of PLA, Chongqing 400035, China.
32705 Unit of PLA, Xi'an, Shaanxi 710086, China.
Comput Intell Neurosci. 2022 Jun 28;2022:3343051. doi: 10.1155/2022/3343051. eCollection 2022.
To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the "cloud, fog, edge, and end" collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.
为了解决移动边缘计算（MEC）资源有限与业务需求激增之间的矛盾，首先，针对智慧校园场景构建了“云-雾-边缘-端”协同架构，并以最小化时延和能耗加权和为目标，提出了联合计算卸载和资源分配的优化模型。其次，为了提高算法的收敛性和跳出局部最优的能力，引入了混沌理论和自适应机制，对教学优化算法（TLBO）的更新方法进行了改进，提出了混沌教学粒子群优化（CTLPSO）算法，并通过与现有改进算法的比较验证了其优势。最后，当模型中的任务数量超过 50 时，卸载成功率优势明显；当任务数量超过 60 时，系统优化效果显著；模型迭代约 100 次即可收敛到最优解。所提出的架构可以有效缓解 MEC 资源有限的问题，所提出的算法在收敛性、稳定性和复杂度方面具有明显优势，优化策略可以提高卸载成功率并降低系统总开销。
