DBAHHO: Deep belief network-based adaptive Harris Hawks optimization for adaptive offloading strategy in mobile edge computing.

Mobile edge computing (MEC) is an emerging paradigm that decreases the computational burden of mobiles by task offloading. MEC is regarded as an effective method to offer computing capacities in close proximities to mobile users. The major issue in MEC is how to offload the heterogeneous task of mobile apps effectively from the user equipment to the MEC host. Some of the existing techniques contain feeble adaptability to new circumstance due to minimum sample effectiveness and require complete retraining. The purpose of MEC is to efficiently solve offloading issues such as network load and latency. In this paper, a deep belief network is proposed for solving the offloading issue in the clusters of numerous service node and several dependencies for a mobile task in huge-scale heterogeneous mobile edge computing. The deep belief network's weight is tuned optimally using Adaptive Harris Hawks optimization (AHHO) algorithm for solving the offloading issue optimally in the MEC environment. An AHHO algorithm is employed to improve the search performances of the standard algorithm. The limitations of the standard algorithm are poor stability among exploitation as well as exploration. Hence, two schemes such as the Gaussian mutation scheme and the cuckoo search are combined so as to form an adaptive Harris hawks optimization to enhance this stability. The task offloading issues are implemented using Google cluster trace and iFogSim. Furthermore, the simulation results depict that the offloading scheme based on the deep belief network-based adaptive Harris Hawks optimization approach has better results with respect to measures such as load balancing, energy consumption, average execution time, and latency than any other approach. [ABSTRACT FROM AUTHOR]