A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments

Mobile Edge Computing (MEC) has arisen as a promising computing paradigm consisting of three tiers: Smart Mobile Devices (SMDs), fog nodes, and the cloud. The MEC enables computational offloading and execution schedules to cope with the problems of insufficient resources for the SMDs and the computational tasks' deadlines. The offloading problem determines in what order and source of the network the tasks should be performed to minimize execution time and power consumption. The main aim of the current paper is to reduce execution time and energy consumption by optimizing tasks' offloading and scheduling in MEC networks. As a result, the task scheduling and offloading are modeled as an optimization problem. Then, an enhanced hybridization of Artificial Ecosystem-based Optimization (AEO) and Arithmetic Optimization Algorithm (AOA), named E-AEO-AOA, is presented to optimize it. In the E-AEO-AOA, the AOA and AEO algorithms are initially discretized. Next, the Q-learning strategy is modified and recruited to hybridize the algorithms in a complementary manner. Subsequently, chaos theory is utilized in a local search procedure to enhance the exploitation capability of the E-AEO-AOA. Eventually, the performance of E-AEO-AOA is examined on fifteen MEC networks. In the experiments, the E-AEO-AOA is compared with AEO, AO, AOA, JS, MRFO, STOA, SCA, and TSA algorithms statistically. Besides, the algorithms' convergence rate and solutions dispersity are visually compared. Moreover, the algorithms are compared by the Wilcoxon signed-rank test. The experimental results indicate that the E-AEO-AOA surpassed competitor algorithms in 90% of cases. Likewise, in 6% of the cases, the E-AEO-AOA produced the same results as AEO, AOA and MRFO.