Co-MAPPO: Enabling Horizontal Collaborative Task Offloading in Multi-access Edge Computing

SUN Wenfei; LI Dekang; LU Xianling

doi:10.11999/JEIT260089

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SUN Wenfei, LI Dekang, LU Xianling. Co-MAPPO: Enabling Horizontal Collaborative Task Offloading in Multi-access Edge Computing[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260089

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SUN Wenfei, LI Dekang, LU Xianling. Co-MAPPO: Enabling Horizontal Collaborative Task Offloading in Multi-access Edge Computing[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260089

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Co-MAPPO: Enabling Horizontal Collaborative Task Offloading in Multi-access Edge Computing

doi: 10.11999/JEIT260089 cstr: 32379.14.JEIT260089

School of Internet of Things, Jiangnan University, Wuxi 214122, China

Funds: The National Natural Science Foundation of China (61773181)

Received Date: 2026-01-26
Accepted Date: 2026-09-15
Rev Recd Date: 2026-09-05

Available Online: 2026-09-24

Abstract

Abstract

Objective The rapid growth of the Internet of Things (IoT) has enabled many emerging applications with stringent latency requirements. In Multi-access Edge Computing (MEC), vertical task offloading from terminal devices to an Edge Node (EN) is a key approach to meeting these requirements. However, each EN serves a limited coverage area, preventing terminal devices outside that area from readily accessing its computing resources and resulting in load imbalances across ENs. Existing vertical task offloading methods primarily optimize terminal-side metrics, such as latency and energy consumption, while giving insufficient attention to load balancing among ENs. Although Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is widely used for vertical task offloading, it is better suited to environments involving both cooperation and competition, whereas horizontal task offloading among ENs constitutes a fully cooperative scenario. Furthermore, conventional vertical task offloading strategies based on Internet Protocol (IP) networks face challenges related to partial observability and limited global information because of the distributed architecture of IP infrastructure. To address these limitations, Software-Defined Networking (SDN) is introduced to provide a global network view and mitigate information asymmetry. A task offloading strategy based on Collaborative Multi-Agent Proximal Policy Optimization (Co-MAPPO) is proposed to support horizontal task offloading and improve load balancing across ENs. Methods An SDN-assisted MEC framework for horizontal task offloading is established, comprising a multi-hop routing communication architecture and a multi-timeslot computation framework. The SDN controller assigns a virtual agent to each EN. A load index is defined as the task data volume per unit frequency of the Central Processing Unit (CPU) for each EN at each timeslot, expressed in Mb/GHz. The variance of the EN load indices is used as the load-balancing index, with a smaller variance indicating more balanced loads across ENs. A multi-objective optimization problem that jointly minimizes system-wide task latency and the load-balancing index is formulated as a stochastic integer programming problem. Subsequently, the offloading process is formulated as a Partially Observable Markov Decision Process (POMDP), and a Co-MAPPO-based task offloading strategy is developed. Because cooperative agents share a common state-value function, a shared critic network is used to estimate the global state-value function. To accommodate agent-specific observations and actions, a shared actor network maps each agent’s encoded local observation to an action probability distribution. An observation one-hot encoding scheme places each agent’s local observation in an agent-specific segment of a zero-padded vector, allowing the shared actor network to distinguish among ENs. During training, the global system state is concatenated with each agent’s local observation and supplied to the shared critic network. This state augmentation enables the critic to account for agent-specific information and mitigates the effects of partial observability on training stability. The SDN controller aggregates information on task data volumes, transmission-queue delays, computation-queue delays, network topology, and link bandwidths to construct the global system state. It computes agent-specific actor and critic losses, aggregates the local losses into global losses, and updates the shared network parameters online. These mechanisms iteratively refine the offloading policy to reduce latency while balancing computational loads across ENs. Results and Discussions The Co-MAPPO strategy exhibits superior convergence performance and outperforms the comparison strategies under different task data volumes and network conditions. (1) Compared with QMIX and the baseline strategies, Co-MAPPO converges faster, exhibits more stable policy updates, and achieves better converged performance. Its clipped proximal policy optimization mechanism limits the magnitude of policy updates and helps stabilize training (Fig. 3). Co-MAPPO also enables horizontal collaboration by offloading computationally intensive tasks from ENs with lower computing capacity to those with higher capacity (Fig. 4). (2) As task data volume increases, Co-MAPPO maintains lower average latency than the comparison strategies and exhibits the smallest increase in latency. Under the highest task loads, it reduces latency by 40.3%–84.3% relative to the other strategies. Its load-balancing index also remains lower than those of the comparison methods, without abrupt increases under heavy workloads (Fig. 5). (3) As transmission bandwidth decreases, system latency increases, but Co-MAPPO retains a clear performance advantage. Under the lowest-bandwidth conditions, it reduces latency by 62.2%–88.5% relative to the other strategies. Its load-balancing index increases gradually as bandwidth becomes constrained but remains lower than those of the comparison methods (Fig. 6). (4) When tasks are offloaded from ENs with lower computing capacity to those with higher capacity, both latency and the load-balancing index decrease, indicating a positive correlation between these two metrics. In contrast, when the policy prioritizes load balancing without adequately accounting for transmission bandwidth and network topology, communication latency can increase even as the load-balancing index decreases, indicating a negative correlation between the two metrics (Figs. 7 and 8). Conclusions The SDN-assisted Co-MAPPO strategy uses a global network view to adapt to changes in network topology and maintain low latency while balancing EN loads under different task data volumes and transmission bandwidths. The results further show that the relationship between system latency and the load-balancing index depends on offloading conditions. (1) Positive-correlation phase: Offloading tasks from ENs with lower computing capacity to those with higher capacity reduces both latency and the load-balancing index, indicating a positive correlation between the two metrics. (2) Negative-correlation phase: Prioritizing load balancing without adequately accounting for transmission bandwidth limitations and network topology can increase communication latency even when the load-balancing index decreases, indicating a negative correlation between the two metrics. These findings indicate that effective horizontal task offloading requires joint consideration of computing capacity, network conditions, and load distribution.

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