Joint Heterogeneous Server Placement and Application Configuration in Edge Computing The rapid development of the Internet of Things (IoT) has brought profound changes in the cloud computing paradigm. One promising computing model in IoTrelated applications is edge computing, which can decrease the request response time by deploying edge servers close to IoT devices. Two fundamental problems in edge computing include how to place a limited number of edge servers on the candidate locations (e.g., the Access Points) and how to configure the applications on each server. In this paper, we jointly study the edge server placement and the application configuration to minimize the weighted sum of the service cost and edge server opening cost. We propose a localsearch based algorithm, named SPAC, to solve the problem efficiently with its approximation ratio analyzed. Extensive simulations on Google data traces demonstrate SPAC reduces the total cost by up to 60% compared with stateoftheart methods, and outperforms the baselines consistently in different parameter settings.

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.