Edge Computing (EC) extends computing functionalities from remote cloud data centers to the proximity of end-user devices at the network edges, thereby reducing application response time. However, existing solutions face challenges in scalability, efficient resource management, and near real-time adaptation in dynamic environments. In this paper, we jointly investigate the application placement and load distribution challenges in an EC-enabled mobile network. We propose a hierarchical distributed Limited Look-Ahead Control (LLC) approach that mitigates centralized control bottlenecks by breaking down the overall decision problem into a series of local problems solved cooperatively through a two-tier architecture. The global controller processes system-wide information and sets local constraints, while local controllers make autonomous decisions coordinated through mutual information exchange. Utilizing LLC allows for anticipation and adaptation to load fluctuations. Even though the results indicate that the trade-off between system performance and scalable decisions depends on how the overall problem is decomposed, our distributed solution significantly reduces the controller’s execution time compared to a centralized approach.

中文翻译：

---

边缘 计算中应用程序放置和负载分配的分层控制


边缘计算 （EC） 将计算功能从远程云数据中心扩展到网络边缘的最终用户设备附近，从而缩短应用程序响应时间。然而，现有解决方案在可扩展性、高效资源管理和动态环境中近乎实时的适应方面面临挑战。在本文中，我们共同研究了支持 EC 的移动网络中的应用放置和负载分配挑战。我们提出了一种分层分布式有限前瞻控制 （LLC） 方法，通过将整体决策问题分解为一系列通过两层架构合作解决的局部问题来缓解集中控制瓶颈。全局控制器处理系统范围的信息并设置本地约束，而本地控制器则通过相互信息交换做出自主决策。利用 LLC 可以预测和适应负载波动。尽管结果表明系统性能和可扩展决策之间的权衡取决于整体问题的分解方式，但与集中式方法相比，我们的分布式解决方案显著缩短了控制器的执行时间。