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Mobile edge computing (MEC) is a promising paradigm to accommodate theincreasingly prosperous delay-sensitive and computation-intensive applicationsin 5G systems. To achieve optimum computation performance in a dynamic MECenvironment, mobile devices often need to make online decisions on whether tooffload the computation tasks to nearby edge terminals under the uncertainty offuture system information (e.g., random wireless channel gain and taskarrivals). The design of an efficient online offloading algorithm ischallenging. On one hand, the fast-varying edge environment requires frequentlysolving a hard combinatorial optimization problem where the integer offloadingdecision and continuous resource allocation variables are strongly coupled. Onthe other hand, the uncertainty of future system states makes it hard for theonline decisions to satisfy long-term system constraints. To address thesechallenges, this article overviews the existing methods and introduces a novelframework that efficiently integrates model-based optimization and model-freelearning techniques. Besides, we suggest some promising future researchdirections of online computation offloading control in MEC networks.

An Integrated Optimization-Learning Framework for Online Combinatorial Computation Offloading in MEC Networks