Mobile edge computing (MEC) is envisioned as a prospective technology that supports latency-critical and computation-intensive applications by using storage and computation resources in network edges. The advantages of this technology are trapped in limited edge cloud resources, and one of the prime challenges is how to allocate available edge cloud resources to satisfy user requests. However, previous works usually optimize service (data&code) placement and request routing simultaneously within the same timescale, ignoring the fact that frequent service replacement will incur expensive operating expenses. In this paper, we jointly optimize service placement and request routing in the MEC network for data analysis applications, under the constraints of computation and storage resource. In particular, the Cloud Radio Access Network (C-RAN) architecture is applied to pool available resources and realize load balancing among edge clouds. In addition, we adopt a two timescale framework to reduce high operating expenses caused by frequent cross-cloud service replication and replica deletion. Then, we develop a greedy-based approximation algorithm for service placement subproblem and a linear programming (LP) relaxation-based heuristic algorithm for request routing subproblem, respectively. Finally, the numerical results demonstrate that our proposed solution reaches 90% of the optimal performance in services homogeneous case and 76% in services heterogeneous case.

移动边缘计算 (MEC) 被设想为一种前瞻性技术，通过在网络边缘使用存储和计算资源来支持延迟关键和计算密集型应用程序。该技术的优势在于边缘云资源有限，其中主要挑战之一是如何分配可用的边缘云资源来满足用户请求。然而，以前的工作通常在相同的时间尺度内同时优化服务（数据和代码）放置和请求路由，忽略了频繁更换服务会产生昂贵的运营费用的事实。在本文中，我们在计算和存储资源的约束下，针对数据分析应用联合优化 MEC 网络中的服务放置和请求路由。特别是，云无线接入网（C-RAN）架构用于池化可用资源，实现边缘云之间的负载均衡。此外，我们采用了两个时间尺度的框架来减少频繁的跨云服务复制和副本删除带来的高额运营费用。然后，我们分别针对服务放置子问题开发了一种基于贪婪的逼近算法和针对请求路由子问题的基于线性规划 (LP) 松弛的启发式算法。最后，数值结果表明，我们提出的解决方案在服务同质情况下达到了 90% 的最佳性能，在服务异构情况下达到了 76%。我们采用了两个时间尺度的框架来减少频繁的跨云服务复制和副本删除带来的高额运营费用。然后，我们分别为服务放置子问题开发了一种基于贪婪的逼近算法，并为请求路由子问题开发了一种基于线性规划（LP）松弛的启发式算法。最后，数值结果表明，我们提出的解决方案在服务同质情况下达到了 90% 的最佳性能，在服务异构情况下达到了 76%。我们采用了两个时间尺度的框架来减少频繁的跨云服务复制和副本删除带来的高额运营费用。然后，我们分别针对服务放置子问题开发了一种基于贪婪的逼近算法和针对请求路由子问题的基于线性规划 (LP) 松弛的启发式算法。最后，数值结果表明，我们提出的解决方案在服务同质情况下达到了 90% 的最佳性能，在服务异构情况下达到了 76%。