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To satisfy the requirements of content distribution in computation-intensive and delay-sensitive services, this paper presents a novel joint task offloading and content caching (JTOCC) scheme in multi-cell multi-carrier non-orthogonal multiple-access (MCMC-NOMA)-assisted cloud-edge-terminal cooperation networks. Based on queuing theory, we formulate a delay minimization model that aggregates users’ requests to reduce repeated content delivery. To minimize network latency, the model is decomposed into three subproblems: task offloading, user clustering and communication resource allocation, and cache state updating. In each slot, the task offloading subproblem is solved utilizing deep reinforcement learning (DRL) under a resource-constrained cloud-edge-terminal setting. During a transition between slots, mobile terminals are grouped using K-means-based user clustering, and the allocations of the subchannels and transmit power are optimized utilizing matching theory and successive convex approximation (SCA), respectively. Contents cached at the network nodes are updated, according to long-short-term memory (LSTM)-based predicted popularity. Simulations show that the proposed JTOCC model achieves lower-delay content distribution than its existing counterparts in cloud-edge-terminal cooperation environments, and converges fast in heterogeneous networks.
Fang et al. (Fri,) studied this question.