Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under the stochastic information scenario. Traditional approaches often treat these decisions as equal, disregarding the differences in information realization. However, in practice, the ES deployment decision must be made in advance and remain unchanged, prior to the complete realization of information, whereas the decisions regarding service placement and computation task offloading can be made and adjusted in real-time after information is fully realized. To address such temporal coupling between decisions and information realization, we introduce the stochastic programming (SP) framework, which involves a strategic-layer for deciding ES deployment based on (incomplete) stochastic information and a tactical-layer for deciding service placement and task offloading based on complete information realization. The problem is challenging due to the different timescales of two layers' decisions. To overcome this challenge, we propose a multi-timescale SP framework, which includes a large timescale (called period) for strategic-layer decision-making and a small timescale (called slot) for tactical-layer decision making. Moreover, we design a Lyapunov-based algorithm to solve the tactical layer problem at each time slot, and a Markov approximation algorithm to solve the strategic-layer problem in every time period. Simulation results demonstrate that our proposed solution significantly outperforms existing benchmarks that overlook the coupling between decisions and information realization, achieving up to 56% reduction in total system cost.

Energy efficiency (EE) is vital for 5G networks to manage the increased traffic demand while minimizing operational costs and reducing environmental impact. Optimizing energy usage also supports scalability and helps meet regulatory sustainability goals as network infrastructure expands. In a practical commercial 5G network, a widely-used method to reduce energy consumption is to dynamically shut down underutilization cells and reduce the overlapping cell coverage based on real-time traffic patterns. However, the inherent uncertainty of traffic demands, coupled with the unknown distribution function, significantly complicates the optimization of cell shutdown strategies, rendering both conventional deterministic and stochastic optimization methods ineffective. To tackle these challenges, we propose a distributionally robust optimization framework for EE optimization that does not rely on distributional knowledge. First, we construct a data-driven uncertainty set to model traffic distribution and apply Lagrangian duality to transform the infinite-dimensional optimization problem into a more tractable finite-dimensional one. Then, we employ a Bayesian optimization algorithm to efficiently solve this reformulated problem, which involves a mixed space of high-dimensional combinatorial cell shutdown and continuous Lagrangian multipliers, even with constrained black-box evaluations. Simulations on real-world field data show that our proposed solution outperforms existing benchmarks, achieving energy efficiency improvements ranging from 0.7 % to 11.05 %.

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%.