A Stochastic Programming Approach for Joint Edge Server Deployment and Computation Offloading
Huaizhe Liu, Z. R. Wang, Jiaqi Wu, Lin Xia Gao · 2023
Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, via providing computation services at the network edge that approximate end-users. In this work, we focus on the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under stochastic information scenario. In traditional solutions, these decisions are often treated equally without considering differences in the information realization. In practice, however, the ES deployment decision needs to be made in advance before the complete information is realized, while the service placement and computation task offloading decisions can be made after the complete information is realized. To capture the time coupling between different decisions and information realizations, we formulate a two-layer stochastic programming (SP) problem, which consists of a strategic-layer decision for ES deployment, and a tactical-layer decision for service placement and computation task offloading. The strategic-layer decision will be made based on the stochastic information (i.e., before the complete information is realized), while the tactical-layer decision will be made based on every information realization. The problem is very challenging due to the large number of information realizations and the corresponding tactical-layer decisions. To solve the problem effectively, we propose a Sample Average Approximate (SAA) method to approximate the optimal solution, which involves generating a large number of randomly sampled information scenarios and using their averages to estimate the expected value of the objective function. Numerical simulations show that our proposed SP approach outperforms the traditional solutions that do not consider the coupling between decisions and information realizations. Moreover, compared with the ideal benchmark solution that assumes complete information, our proposed SP approach only results in a small performance degradation of 1.03%$\sim$6.26%.