Optimization of Service Placement with Fairness Due to the large increase of Internet of Things (IoT) devices in the last years, the cloud has been proved unsuitable to deal with the high demand of requests generated by them. To deal with this, fog computing was proposed in order to provide closer computing services in a distributed manner, acting as a middle layer between IoT devices and the cloud. However, these devices have low energy and low computational power. Therefore, strategies to distribute requested services, bundled in a set of applications, are of particular interest. Furthermore, these applications have deadlines that may be short, which have to be met. Hence, models and algorithms are needed to place the requested services in order to meet the deadlines while using the fog as distributed as possible. In this paper, we present a Mixed Integer Linear Programming (MILP) formulation with two main objectives: maximize the fog usage and maximize the fairness throughout the system (fog and cloud). We also propose an heuristic that obtains approximate solutions as fast as possible. We compare both approaches using a set of randomly generated applications composed by different deadlines and services.

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.