Placing applications in mobile edge computing servers presents a complex challenge involving many servers, users, and their requests. Existing algorithms take a long time to solve high-dimensional problems with significant uncertainty scenarios. Therefore, an efficient approach is required to maximize the quality of service while considering all technical constraints. One of these approaches is machine learning, which emulates optimal solutions for application placement in edge servers. Machine learning models are expected to learn how to allocate user requests to servers based on the spatial positions of users and servers. In this study, the problem is formulated as a two-stage stochastic programming. A sufficient amount of training records is generated by varying parameters such as user locations, their request rates, and solving the optimization model. Then, based on the distance features of each user from the available servers and their request rates, machine learning models generate decision variables for the first stage of the stochastic optimization model, which is the user-to-server request allocation, and are employed as independent decision agents that reliably mimic the optimization model. Support Vector Machines (SVM) and Multi-layer Perceptron (MLP) are used in this research to achieve practical decisions from the stochastic optimization models. The performance of each model has shown an execution effectiveness of over 80%. This research aims to provide a more efficient approach for tackling high-dimensional problems and scenarios with uncertainties in mobile edge computing by leveraging machine learning models for optimal decision-making in request allocation to edge servers. These results suggest that machine-learning models can significantly improve solution times compared to conventional approaches.

翻译：将应用部署到移动边缘计算服务器中面临着涉及众多服务器、用户及其请求的复杂挑战。现有算法在解决具有显著不确定性场景的高维问题时耗时较长。因此，在满足所有技术约束的前提下，需要一种高效的方法来最大化服务质量。其中一种方法是机器学习，它模拟边缘服务器中应用放置的最优解。机器学习模型需要学习如何根据用户和服务器的空间位置将用户请求分配给服务器。本研究将该问题建模为两阶段随机规划。通过改变用户位置、请求率等参数并求解优化模型，生成充足的训练数据集。随后，基于每个用户与可用服务器的距离特征及其请求率，机器学习模型生成随机优化模型第一阶段（即用户到服务器的请求分配）的决策变量，并作为独立决策代理，可靠地模拟优化模型。本研究采用支持向量机（SVM）和多层感知机（MLP）从随机优化模型中获取实用决策，两种模型的执行效率均超过80%。本研究旨在通过利用机器学习模型实现边缘服务器请求分配的最优决策，为处理移动边缘计算中的高维问题和不确定场景提供更高效的方法。结果表明，与传统方法相比，机器学习模型可显著提升求解速度。