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By offloading computationally intensive tasks to the edge cloud, the mobile edge computing (MEC) technique has the potential to realize the critical millisecond-scale latency requirement of next generation mobile services. In this paper, we study heterogeneous tasks offloading in an orthogonal frequency division multiple access (OFDMA) based cloud radio access (C-RAN) network with an integrated MEC server. A joint subcarrier, power allocation and tasks partition problem is formulated to minimize the delay of each user. In order to tackle the intractable optimization problem, an improved hybrid-fitness function evolutionary (HFFE) algorithm is proposed by relaxing the channel allocation indicators into continuous variables. Simulation results indicate that the performance of the proposed algorithm can approach the optimal result obtained by exhaustive search but with much lower complexity.
Wang et al. (Mon,) studied this question.