论文标题
最佳的卸载策略，用于通过平均场游戏和控制
Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control
论文作者
论文摘要
在自私和完全合作的环境中，在异质边缘表面场景中的任务最佳卸载都是极大的实际兴趣。实际上，这样的系统通常非常大，从合作的Optima或Nash Equilibria棘手方面呈现精确的解决方案。为此，我们采用了一般的平均场配方，以解决无限大型系统极限的竞争和合作卸载问题。我们为限制解决方案的近似属性提供了理论保证，并以数值为单位解决了所得的平均场问题。此外，我们通过数值验证解决方案，发现我们的近似值对于具有数十个边缘设备的系统是准确的。结果，我们获得了与许多用户在大型边缘计算方案中卸载策略设计的可访问方法。
The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such systems are typically very large, rendering exact solutions in terms of cooperative optima or Nash equilibria intractable. For this purpose, we adopt a general mean-field formulation in order to solve the competitive and cooperative offloading problems in the limit of infinitely large systems. We give theoretical guarantees for the approximation properties of the limiting solution and solve the resulting mean-field problems numerically. Furthermore, we verify our solutions numerically and find that our approximations are accurate for systems with dozens of edge devices. As a result, we obtain a tractable approach to the design of offloading strategies in large edge-computing scenarios with many users.
