A Cloudlet Placement Method Based on Birch in Wireless Metropolitan Area Network Mobile edge computing was proposed to push data centers towards network edges for reducing the network latency of delivering cloud services to mobile devices. Cloudlet is one type of edge servers which can provide abundant resources to mobile users. However, there are a large number of mobile users in Wireless Metropolitan Area Network (WMAN), and these users are always on the moving. Meanwhile, the number of cloudlets is limited. And therefore how to deploy cloudlets in WMAN is critical. In view of these challenges, a cloudlet placement method based on Balanced Iterative Reducing and Clustering Using Hierarchies is proposed in this paper. Compared to other methods, our proposed method not only can cover most of mobile devices, but also solve user mobility issues. In addition, a load balancing process is used in our proposed method which can balance each cluster better.

Generalized CostAware Cloudlet Placement for Vehicular Edge Computing Systems One of the wellknown challenges in Edge Computing is strategic placement of cloudlets. The fundamental goals of this challenge are to minimize the deployment cost of cloudlets and to guarantee minimum latency for users of edge services. However, building cloudlet infrastructure may not be feasible in many situations and areas (e.g., disaster situations, unexpected surge in demand, and remote rural areas). Vehicular edge computing, VEC, introduces mobile cloudlets to augment edge computing capacity, enhance its coverage, and reduce latency significantly. However, efficient cloudlet placement is even more critical in VEC as it is not a longterm decision and needs to be repeated over time. In this paper, we address this challenge by designing a generalized costaware cloudlet placement approach that places a set of heterogeneous cloudlets in a region and fully maps user applications to appropriate cloudlets while ensuring their latency requirements. We first formulate the problem as a multiobjective integer programming model in a general deployment scenario. This is a computationally NPhard problem. To tackle its intractability, we then propose a genetic algorithmbased approach, GACP. We investigate the effectiveness of GACP by performing extensive experiments on multiple deployment scenarios based on New York City OpenData. The results show that GACP obtains close to optimal cost placement in significantly reduced time.