Risk-Aware Application Placement in Mobile Edge Computing Systems: A Learning-based Optimization Approach
Hossein Badri, Tayebeh Bahreini, Daniel Grosu, Kai Yang · 2020
In this paper, we address the problem of application placement in MEC systems that takes into account the risk of exceeding the energy budget of the edge servers. We formulate the problem as a chance-constrained program, where the objective is to maximize the total quality of service in the system, while keeping the expected risk of exceeding the edge servers' energy budget within an acceptable threshold. We develop a learning-based method to solve the problem which requires a very small execution time for large size instances. We evaluate the performance of the proposed method by conducting an extensive experimental analysis.