Game-Theoretic Bandwidth Allocation and Task Offloading in Cloud–Edge Collaboration

The rapid growth of Internet of Things (IoT) devices has imposed higher demands on computational capabilities, which traditional cloud computing struggles to meet in real-time scenarios due to latency issues. Mobile edge computing (MEC) addresses these challenges by processing data at the network edge, thereby reducing latency and enhancing computational efficiency. However, MEC alone is insufficient for handling complex tasks, requiring more robust solutions. This paper proposes a hybrid cloud-edge computing framework that enhances system performance by integrating cloud and edge computing. A game-theoretic model is used to optimize wireless bandwidth allocation, and a Stackelberg game mechanism is introduced to incentivize task offloading. This approach orchestrates resource allocation and task offloading dynamics through game theory, ensuring cost minimization and delay requirements are met while fostering cloud-edge collaboration. Theoretical analysis demonstrates the existence of Nash equilibria in both layers of the game, ensuring the system’s stability and effectiveness in complex environments. Based on this, the GA-based resource allocation and offloading (GRAO) algorithm, and the iterative game-theoretic offloading (IGTO) algorithm are proposed. Experimental results validate the proposed algorithms, showing that the IGTO algorithm reduces the average cost for mobile devices (MDs) by 49.8% compared to the best baseline, while enhancing overall performance for both MEC servers and the cloud.