  As data traffic volume continues to increase, caching of popular content at
strategic network locations closer to the end user can enhance not only user
experience but ease the utilization of highly congested links in the network. A
key challenge in the area of proactive caching is finding the optimal locations
to host the popular content items under various optimization criteria. These
problems are combinatorial in nature and therefore finding optimal and/or near
optimal decisions is computationally expensive. In this paper a framework is
proposed to reduce the computational complexity of the underlying integer
mathematical program by first predicting decision variables related to optimal
locations using a deep convolutional neural network (CNN). The CNN is trained
in an offline manner with optimal solutions and is then used to feed a much
smaller optimization problems which is amenable for real-time decision making.
Numerical investigations reveal that the proposed approach can provide in an
online manner high quality decision making; a feature which is crucially
important for real-world implementations.
