H. Badri, Tayebeh Bahreini, Daniel Grosu, Kai Yang
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Risk-Based Optimization of Resource Provisioning in Mobile Edge Computing
In this paper, we propose a risked-based optimization approach to resource provisioning in MEC systems with the aim of taking into account the risk of overloading of edge servers when making allocation decisions. Assuming that resource requirements of mobile applications are stochastic parameters, we formulate the problem as a chance-constrained stochastic program. In order to solve the problem in reasonable amount of time, we employ the Sample Average Approximation (SAA) method. We evaluate the efficiency of the proposed approach by conducting an experimental analysis on instances with different problem settings.
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