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<p>In edge computing environments with multiple edge servers, load imbalance is common due to varying user densities. High-density regions often lead to overloaded servers, while low-density areas result in resource underutilization, reducing overall system efficiency. To address this, task migration from high-load to low-load servers is a feasible solution. However, as edge servers are often self-interested, they may not willingly offer computing services for free. To incentivize cooperation, we propose a task migration strategy based on a double auction mechanism, termed <b>Double Auction Task Migration Strategy (DATMS)</b>. In this strategy, users offload tasks to their nearest servers. High-load servers (sellers) determine selling prices and task quantities to migrate, while low-load servers (buyers) decide their bid prices for the migrated tasks. The double auction mechanism then matches sellers with buyers to maximize mutual benefits and achieve system-wide load balancing. The sellers’ decision-making process is modeled as a Markov Decision Process (MDP) and solved using the Deep <i>Q</i>-Network (<b>DQN</b>) algorithm. For buyers, who operate under partial observability and continuous action spaces, we model the problem as a Partially Observable Markov Decision Process (POMDP) and adopt the Multi-Agent Deep Deterministic Policy Gradient (<b>MADDPG</b>) algorithm. The integration of <b>DQN</b>, <b>MADDPG</b>, and the double auction mechanism forms the complete DATMS strategy. Extensive experiments demonstrate that <b>DATMS</b> significantly outperforms four benchmark strategies in terms of long-term load balancing across the entire system.</p>

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Double auction based task migration for load balancing among multiple edge servers

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Mengfei Duan,
  • Bing Shi,
  • Ge Mao

摘要

In edge computing environments with multiple edge servers, load imbalance is common due to varying user densities. High-density regions often lead to overloaded servers, while low-density areas result in resource underutilization, reducing overall system efficiency. To address this, task migration from high-load to low-load servers is a feasible solution. However, as edge servers are often self-interested, they may not willingly offer computing services for free. To incentivize cooperation, we propose a task migration strategy based on a double auction mechanism, termed Double Auction Task Migration Strategy (DATMS). In this strategy, users offload tasks to their nearest servers. High-load servers (sellers) determine selling prices and task quantities to migrate, while low-load servers (buyers) decide their bid prices for the migrated tasks. The double auction mechanism then matches sellers with buyers to maximize mutual benefits and achieve system-wide load balancing. The sellers’ decision-making process is modeled as a Markov Decision Process (MDP) and solved using the Deep Q-Network (DQN) algorithm. For buyers, who operate under partial observability and continuous action spaces, we model the problem as a Partially Observable Markov Decision Process (POMDP) and adopt the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The integration of DQN, MADDPG, and the double auction mechanism forms the complete DATMS strategy. Extensive experiments demonstrate that DATMS significantly outperforms four benchmark strategies in terms of long-term load balancing across the entire system.