Liu Qi, Tian Zhao, Zhao Guohua, Cui Yong, Lin Yusong
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.
Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China.
PeerJ Comput Sci. 2023 Mar 8;9:e1239. doi: 10.7717/peerj-cs.1239. eCollection 2023.
Computation offloading has effectively solved the problem of terminal devices computing resources limitation in hospitals by shifting the medical image diagnosis task to the edge servers for execution. Appropriate offloading strategies for diagnostic tasks are essential. However, the risk awareness of each user and the multiple expenses associated with processing tasks have been ignored in prior works. In this article, a multi-user multi-objective computation offloading for medical image diagnosis is proposed. First, the prospect theoretic utility function of each user is designed considering the delay, energy consumption, payment, and risk awareness. Second, the computation offloading problem including the above factors is defined as a distributed optimization problem, which with the goal of maximizing the utility of each user. The distributed optimization problem is then transformed into a non-cooperative game among the users. The exact potential game proves that the non-cooperative game has Nash equilibrium points. A low-complexity computation offloading algorithm based on best response dynamics finally is proposed. Detailed numerical experiments demonstrate the impact of different parameters and convergence in the algorithm on the utility function. The result shows that, compare with four benchmarks and four heuristic algorithms, the proposed algorithm in this article ensures a faster convergence speed and achieves only a 1.14% decrease in the utility value as the number of users increases.
通过将医学图像诊断任务转移到边缘服务器执行，计算卸载有效地解决了医院中终端设备计算资源受限的问题。针对诊断任务制定合适的卸载策略至关重要。然而，先前的研究忽略了每个用户的风险意识以及与处理任务相关的多种费用。本文提出了一种用于医学图像诊断的多用户多目标计算卸载方法。首先，考虑延迟、能耗、支付和风险意识，设计了每个用户的前景理论效用函数。其次，将包含上述因素的计算卸载问题定义为一个分布式优化问题，目标是最大化每个用户的效用。然后，将分布式优化问题转化为用户之间的非合作博弈。精确势博弈证明了该非合作博弈存在纳什均衡点。最后提出了一种基于最佳响应动态的低复杂度计算卸载算法。详细的数值实验展示了算法中不同参数和收敛性对效用函数的影响。结果表明，与四个基准算法和四个启发式算法相比，本文提出的算法确保了更快的收敛速度，并且随着用户数量的增加，效用值仅下降1.14%。
