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Efficient Computation Offloading for Edge-cloud Collaborative Networks

Mobile edge computing is a novel paradigm that provides computing capabilities at the edge of the radio access network close to end devices to support latency-critical applications and services. However, the benefits will be canceled out by the limited computing capacity of edge servers. An edge-cloud paradigm has been studied to improve computing capabilities to solve the above problem. In this paper, a multi-cell edge-cloud architecture is considered to meet the demands of latency-sensitive applications and deal with large-scale data offloading. The optimized offloading scheme is studied to minimize the devices' overhead which is measured as a function of energy consumption and computational cost. We formulate the problem as a Mixed Integer Linear Programming Problem and adopt the Branch-and-Bound algorithm to solve it. Due to the high time overhead of the method, we first transform the problem into a more tractable form and then adopt a learning approach to imitate the branching strategy to improve the Branch-and-Bound algorithm. Experiments of results show that our approach can reduce the time-cost of the Branch-and-Bound and the result is close to the traditional scheme.