Three-Tier Computing Platform Optimization: A Deep Reinforcement Learning Approach

The increasing number of computing platforms is critical with the increasing trend of delay-sensitive complex applications with enormous power consumption. These computing platforms attach themselves to multiple small base stations and macro base stations to optimize system performance if appropriately allocated. The arrival rate of computing tasks is often stochastic under time-varying wireless channel conditions in the mobile edge computing Internet of things (MEC IoT) network, making it challenging to implement an optimal offloading scheme. The user needs to choose the best computing platforms and base stations to minimize the task completion time and consume less power. In addition, the reliability of our system in terms of the number of computing resources (power, CPU cycles) each computing platform consumes to process the user's task efficiently needs to be ascertained. This paper implements a computational task offloading scheme to a high-performance processor through a small base station and a collaborative edge device through macro base stations, considering the system's maximum available processing capacity as one of the optimization constraints. We minimized the latency and energy consumption, which constitute the system's total cost, by optimizing the computing platform choice, base station's choice, and resource allocation (computing, communication, power). We use the actor-critic architecture to implement a Markov decision process that depends on deep reinforcement learning (DRL) to solve the model's problem. Simulation results showed that our proposed scheme achieves significant long-term rewards in latency and energy costs compared with random search, greedy search, deep Q-learning, and the other schemes that implemented either the local computation or edge computation.