Xiaoquan Yi, Haixing Wu, Haozhao Wang, Yichen Li, Yuhua Li, Rui Zhang, Ruixuan Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Mobile edge computing (MEC) has become an effective paradigm to support computation-intensive applications by providing services in close proximity to user devices (UDs). In MEC networks, computation offloading technology is devoted to balancing system load and prolonging UDs' battery life. However, most existing studies on computation offloading take the impractical assumption of the MEC scenario with homogeneous users, ignoring security requirement from certain users. Moreover, with users mobility and task arrivals correlation, most existing computing offloading approaches suffer from inefficient or suboptimal decision making in practical MEC environments. To tackle these issues, by integrating task arrivals correlation within a time slot and environment dynamics between time slots, we propose an adaptive computation offloading scheme based on a collaborative architecture with heterogeneous MEC nodes. First, considering additional security requirement from very important people (VIP) users, we present a novel collaborative architecture by separating edge/cloud servers into public and private nodes. Then, with the architecture, we develop a dynamic computation offloading (DCO) algorithm to realize adaptive computation offloading scheme in MEC environment with mobile users. Particularly, the algorithm involves three stages. 1) By extending Poisson process into Markovian arrival process (MAP), we construct an MAP-based system model to capture the behavior of time-dependent task arrivals and then analyze the system model to derive the system delay in steady state. 2) For the purpose of minimizing the system delay in each time slot, we formulate a computation offloading problem in MEC environment with mobile users. 3) Under a deep reinforcement learning (DRL) framework, by taking the system delay as environmental feedback, we solve the formulated problem and provide offloading decisions in each time slot. We evaluate the performance of DCO algorithm by comparing it with other benchmark algorithms in various application scenarios. Results demonstrate that the proposed DCO algorithm outperforms the compared algorithms in response performance.

How to collaboratively offload tasks between user devices, edge networks (ENs), and cloud data centers is an interesting and challenging research topic. In this paper, we investigate the offloading decision, analytical modeling, and system parameter optimization problem in a collaborative cloud-edge-device environment, aiming to trade off different performance measures. According to the differentiated delay requirements of tasks, we classify the tasks into delay-sensitive and delay-tolerant tasks. To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible, we propose a cloud-edge-device collaborative task offloading scheme, in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy, respectively. We establish a four-dimensional continuous-time Markov chain as the system model. By using the Gauss-Seidel method, we derive the stationary probability distribution of the system model. Accordingly, we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks. Numerical experiments are conducted and analyzed to evaluate the system performance, and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme. Finally, we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.