Dynamic offloading technique for real-time edge-to-cloud computing in heterogeneous MEC–MCC and IoT devices

In the realm of Mobile Cloud Computing (MCC), where mobile environments converge with cloud capabilities to address challenges related to performance, environmental considerations, and security, the complexity of task offloading intensifies with the growing number of cloud providers and application units. While Mobile Edge Computing (MEC) shows promise in enhancing MCC performance, the central research challenge revolves around efficiently determining when to offload computational tasks to the cloud or handle them locally at the edge. It involves finding the optimal balance between utilizing cloud resources and enhancing edge computing performance. This study aims to streamline the decision-making process, seeking a harmonious balance between optimal cloud resource utilization and improved edge computing performance. To this end, we introduce HECOM (Hybrid Edge-to-Cloud Offloading for Heterogeneous Computing), a strategic model that combines three decision-making algorithms and an efficient hybrid computation offloading method. HECOM swiftly identifies optimal offloading servers for individual application units, addressing the challenge of efficient offloading decision-making. Importantly, HECOM provides optimal solutions without waiting for all training processes to conclude. Evaluation results from experiments and simulations affirm that the proposed model exhibits impressive energy efficiency, reducing consumption by 25%, increasing offloading ratio by 30%, and minimizing latency and time delay by 15%, making it ideal for real-time applications. It significantly enhances network resource allocation and achieves a more balanced overall performance profile.