Service Selection Based on Bat Algorithm in Hybrid Cloud-Edge Computing

Handing over edge network data and computation to cloud platforms often results in high bandwidth costs and processing delays, which makes hybrid cloud-edge computing a hot topic for research and application in recent years. If reasonable service selection can be performed on mobile edge gateways, not only can this problem be well solved, but also the energy cost of mobile devices can be reduced. In this paper, we first design a time and energy cost model that considers the latency and energy cost of three components: edge devices, cloud servers, and data transmission, and convert the service selection of minimizing the overall latency under the completion of energy constraints into a nonlinear programming problem. Then we design a bat algorithm to solve the above problem. Furthermore, we design an adaptive chaos bat algorithm to optimize the solution space so that it avoids falling into local optimal solutions. Eventually, simulation results show that the proposed algorithm is superior to other algorithms in terms of overall time delay optimization and has a better stability.