移动边缘计算中资源受限的动态服务部署策略

移动边缘计算（MEC）的出现使移动用户能够以低延迟访问部署在边缘服务器上的服务。然而，MEC仍然存在各种挑战，尤其是服务部署问题。边缘服务器的数量和资源通常是有限的，只能部署数量有限的服务；此外，用户的移动性改变了不同服务在不同地区的流行度。在这种情况下，为动态请求部署合适的服务就成为一个关键问题。针对该问题，通过了解动态用户请求来部署适当的服务以最小化交互延迟，将服务部署问题表述为一个全局优化问题，并提出了一种基于集群划分的资源聚合算法，从而在计算、带宽等资源约束下初步部署合适的服务。此外，考虑动态用户请求对服务流行度及边缘服务器负载的影响，开发了动态调整算法来更新现有服务，以确保服务质量（QoS）始终满足用户期望。通过一系列仿真实验验证了所提出策略的性能。仿真结果表明，与现有基准算法相比，所提出的策略可以降低服务交互延迟并实现更稳定的负载均衡。

Dynamic service deployment strategy in resource constrained mobile edge computing

The emergence of Mobile Edge Computing （MEC） enables mobile users to easily access services deployed on edge servers with low latency. However， there are various challenges in MEC， especially service deployment issues. The number and resources of edge servers are usually limited and only a limited number of services can be deployed on the edge servers； in addition， the mobility of users changes the popularities of different services in different regions. In this context， deploying suitable services for dynamic service requests becomes a critical problem. To address this problem， by deploying appropriate services by awareness of the dynamic user requirements to minimize interaction delay， the service deployment problem was formulated as a global optimization problem， and a cluster-based resource aggregation algorithm was proposed， which initially deployed suitable services under the resource constraints such as computing and bandwidth. Moreover， considering the influence of dynamic user requests on service popularity and edge server load， a dynamic adjustment algorithm was developed to update the existing services to ensure that the Quality of Service （QoS） always met user expectations. The performance of this deployment strategy was verified through a series of simulation experiments. Simulation results show that compared with the existing benchmark algorithms， the proposed strategy can reduce service interaction delay and achieve a more stable load balance.