UPOA: A User Preference Based Latency and Energy Aware Intelligent Offloading Approach for Cloud-Edge Systems

Task offloading has been widely used to extend the battery life of intelligent mobile devices. Existing task offloading approaches, focusing on perfecting the balance between latency and energy consumption, completely ignore the impacts of the user preference caused by low battery anxiety. The existence of low battery anxiety - mobile users' common fear of losing battery energy, especially when the battery energy is already low - causes users to trade high latency for prolonged battery life. Taking into account the user preference impacts on task offloading, we propose a novel offloading approach called UPOA to obtain refined offloading policies between low latency and energy consumption based on user preferences. In UPOA, we start the study by defining a user preference rule that determines users' offloading preferences according to battery energy status. Then, we build a fine-grained task offloading model to delineate the task distribution characteristics of each node in its offloading link. Guided by this model, we develop a task prediction algorithm based on the long-short-term-memory neural network model to provide task predictions that facilitate offloading policies. Lastly, we implement a particle-swarm-optimization-based online offloading algorithm. The offloading algorithm provides the best long-term offloading policies by incorporating the user preference determined by our user preference rule and the task predictions generated by our task prediction algorithm. To quantitatively evaluate the performance of UPOA, we conduct extensive experiments in a real-world cloud-edge environment. We compare UPOA with three state-of-the-art offloading approaches, DRA, DRL-E2D, and MUDRL under various conditions. Experimental results demonstrate that UPOA can make effective policies based on user preferences compared with the existing approaches. UPOA reduces average latency by 12.49% when battery energy is sufficient and extends battery life by 20.14% when battery energy is low.