论文标题
协作边缘计算的社会福利最大化：一种基于强化的学习方法
Social Welfare Maximization for Collaborative Edge Computing: A Deep Reinforcement Learning-Based Approach
论文作者
论文摘要
协作边缘计算（CEC）是一种有效的方法，可以通过将计算任务从繁忙的边缘服务器（ESS）卸载到空闲的方法来改善移动边缘计算（MEC）系统的性能。但是，ESS通常属于不同的MEC服务提供商，因此他们没有动力来帮助他人。为了激励他们之间的合作，本文提出了一种合作机制，可以通过共享其备用计算资源来赚取额外的利润。为了实现最佳资源分配，我们将社会福利最大化问题作为马尔可夫决策过程（MDP），并将其分解为两个阶段，涉及分配和执行后的任务。通过扩展众所周知的深层确定性政策梯度（DDPG）算法来解决第一阶段。在第二阶段，我们首先表明我们只需要确定任务的处理顺序和使用的计算资源。之后，我们提出了动态的编程和深入的增强学习（DRL）算法，分别解决了两种类型的决策。数值结果表明，在各种情况下，我们的算法显着改善了社会福利。
Collaborative Edge Computing (CEC) is an effective method that improves the performance of Mobile Edge Computing (MEC) systems by offloading computation tasks from busy edge servers (ESs) to idle ones. However, ESs usually belong to different MEC service providers so they have no incentive to help others. To motivate cooperation among them, this paper proposes a cooperative mechanism where idle ESs can earn extra profits by sharing their spare computational resources. To achieve the optimal resource allocation, we formulate the social welfare maximization problem as a Markov Decision Process (MDP) and decompose it into two stages involving the allocation and execution of offloaded tasks. The first stage is solved by extending the well-known Deep Deterministic Policy Gradient (DDPG) algorithm. For the second stage, we first show that we only need to decide the processing order of tasks and the utilized computational resources. After that, we propose a dynamic programming and a Deep Reinforcement Learning (DRL)-based algorithm to solve the two types of decisions, respectively. Numerical results indicate that our algorithm significantly improves social welfare under various situations.
