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.

本研究利用机器学习模型对边缘服务器中的请求分配进行优化，提供一种更高效的方法来解决移动边缘计算中的高维问题和不确定性场景，结果显示机器学习模型相对传统方法具有显著的解决时间改进。