Efficient Risk-Averse Request Allocation for Multi-Access Edge Computing

With the evolution of multi-access edge computing (MEC) and 5G communications, diverse services can be flexibly offered by multiple cache-enabled base stations (BSs) in dense deployments. This leads to an important request allocation problem, which studies how to direct user requests among multiple BSs. Efficient request allocation faces several challenges corresponding to the risk associated with the significant uncertainty in the MEC system. Thus, we formulate the request allocation in a risk-averse learning framework to minimize the expected user response delay, and also control the risk measured by the variance of uncertain return. However, solving this risk-averse optimization can be very difficult due to the high computation cost and limited computing resources in the MEC system. This further motivates us to convert the proposed model to a finite-sum composition optimization, and propose a new variant of composition stochastic variance-reduced gradient (C-SVRG) algorithm to accelerate parameter training by estimating the inner function on its linearization. Theoretical analysis proves linear convergence rate and significant complexity reduction of C-SVRG, and simulation results confirm its efficacy.