Summary
Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge computing, it is a significant issue for cost reduction and QoS improvement to place edge clouds at the edge network as a small data center to serve users. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Specifically, we formulate it as a multiobjective optimization problem with objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. To this end, we propose an approximate approach that adopted the K‐means and mixed‐integer quadratic programming. Furthermore, we conduct experiments based on Shanghai Telecom's base station data set and compare our approach with other representative approaches. The results show that our approach performs better to some extent in terms of workload balance and communication delay and validate the proposed approach.
Quality of Service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of Web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of Web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world Web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown Web service QoS values more accurately than other existing approaches.
Vehicles often communicate among different networks in Internet of Vehicles (IoVs). However, existing unstable network statuses and different user preferences result in vehicle frequent vertical handoffs (VHOs). In this paper, we propose a novel VHO method based on a self-selection decision tree for IoVs. We first establish the respective handoff probability distribution of vehicles according to network attributes and movement trend. Then, based on handoff probability distributions and defined user preferences, we propose a novel handoff method by the selfselection decision tree for IoVs. Finally, we also present a feedback decision method according to the feedback of vehicle handoff, to improve next handoff quality when vehicle movement trend and vehicle service status change. Simulation results show that the proposed method not only supports the VHO among Wireless Access in Vehicular Environments, Worldwide Interoperability for Microwave Access, and third-generation cellular but also reduces switching times and ensures the network update rate and the vehicles' service quality.
Index Terms-Decision tree, feedback decision, Internet of Vehicles (IoVs), self-selection, vertical handoff (VHO).
1932-8184
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