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 development of intelligent IoT has spawned numerous novel applications with low-latency requirements, where vertical task offloading from terminal devices to Edge Node (EN) serves as a critical technology for Multi-access Edge Computing (MEC) to ensure latency guarantees. Typically constrained by limited-service coverage areas, ENs struggle to provide computational resources to terminal devices outside their service regions, resulting in significant load disparities among different nodes. However, existing vertical task offloading approaches primarily optimize terminal-side metrics such as latency and energy consumption, while insufficiently addressing load balancing at the EN layer. Although reinforcement learning algorithms like the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) have been widely adopted for vertical task offloading, they are better suited for competitive-cooperative hybrid environments, whereas horizontal task offloading between ENs constitutes a fully cooperative scenario. Furthermore, conventional vertical task offloading strategies relying on IP networks inherently face challenges of partial observability and global information deficiency due to the distributed architecture of IP infrastructure. To address these limitations, this research introduces Software-Defined Networking (SDN) technology to establish a global view for resolving information asymmetry issues, and proposes a collaborative multi-agent proximal policy optimization (Co-MAPPO)-based task offloading strategy that effectively supports horizontal task offloading, thereby achieving comprehensive EN load balancing. Methods This research first integrates SDN technology into MEC-enabled horizontal task offloading, establishing a multi-hop routing communication architecture and a multi-timeslot computational framework where the SDN controller deploys virtual agents across EN. A load index metric is proposed with its variance serving as the quantitative measure for load balancing, while the multi-objective optimization problem simultaneously addressing task processing latency reduction and system-wide load balancing is formulated as a stochastic integer programming model. Subsequently, a Partially Observable Markov Decision Process (POMDP) is developed for collaborative horizontal task offloading, accompanied by the Co-MAPPO based task offloading strategy. Given the homogeneous state-value functions inherent in cooperative agents, a shared Critic network is implemented to approximate the global state-value function. To address heterogeneous observations and actions across agents, a shared Actor network is developed to approximate the global policy function. Agent identification capability is enhanced through one-hot encoding of observational data and state augmentation techniques, enabling the shared Actor-Critic network to effectively discriminate between heterogeneous agents. The SDN controller aggregates decentralized local observations to reconstruct global system states, computes compound loss functions encompassing both local and global optimization objectives from the shared Actor-Critic network outputs, and executes real-time parameter updates through online learning mechanisms. This iterative optimization process progressively refines the task offloading policy towards achieving balanced computational load distribution across EN. Results and Discussions The Co-MAPPO strategy demonstrates superior convergence performance and outperforms comparative task offloading approaches across varying task data volumes and network environments. (1) Co-MAPPO exhibits advantages over QMix and baseline strategies in convergence speed, policy update stability, and converged outcomes, attributable to its integration of proximal policy optimization that ensures monotonic policy improvement during training (Fig.3). Furthermore, the strategy achieves horizontal collaboration among EN by offloading computationally intensive tasks from resource-constrained ENs to high-capacity ENs (Fig.4). (2) As task data volume increases, Co-MAPPO maintains lower average latency compared to other strategies, with the smallest latency escalation rate. At peak data loads, Co-MAPPO reduces latency by 40.3%～84.3% relative to other strategies. Concurrently, its load balancing index remains consistently lower than comparative methods without abrupt increases under heavy workloads (Fig.5). (3) Although system latency is observed to increase with reduced transmission bandwidth, Co-MAPPO retains significant superiority. Under minimal bandwidth conditions, it achieves latency reductions of 62.2～88.5% compared to other strategies. The load balancing index exhibits gradual growth under bandwidth constraints while maintaining optimal levels (Fig.6). (4) While latency and load balancing demonstrate positive correlation when offloading tasks from low-capacity to high-capacity EN, excessive emphasis on load balancing may inversely increase communication latency due to neglected bandwidth limitations and network topology, thereby establishing a negative correlation under such conditions (Fig.7, Fig.8). Conclusions The Co-MAPPO strategy utilizes the global network view provided by SDN to dynamically perceive network topology, enabling the MEC system to maintain low latency and achieve load balancing under varying task data volumes and network bandwidth conditions. Additionally, a complex coupling relationship exists between latency and load balancing: (1) Positive Correlation Phase: When tasks are offloaded from computationally weak EN to high-capacity EN, latency reduction is achieved alongside improved load balancing, demonstrating a positive correlation between the two metrics. (2) Negative Correlation Phase: While prioritizing load balancing optimization, Co-MAPPO may inadvertently increase communication latency due to unaddressed transmission bandwidth limitations and network topology constraints, resulting in a negative correlation between latency and load balancing performance.

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