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Internet of Things (IoT) applications introduce a set of stringent requirements (e.g., low latency, high bandwidth) to network and computing paradigm. 5G networks are faced with great challenges for supporting IoT services. The centralized cloud computing paradigm also becomes inefficient for those stringent requirements. Only extending spectrum resources cannot solve the problem effectively. Mobile edge computing offers an IT service environment at the Radio Access Network edge and presents great opportunities for the development of IoT applications. With the capability to reduce latency and offer an improved user experience, mobile edge computing becomes a key technology toward 5G. To achieve abundant sharing, complex IoT applications have been implemented as a set of lightweight micro-services that are distributed among containers over the mobile edge network. How to produce the optimal collocation of suitable micro-service for an application in mobile edge computing environment is an important issue that should be addressed. To address this issue, we propose a latency-aware micro-service mashup approach in this paper. Firstly, the problem is formulated into an integer nonlinear programming. Then, we prove the NP-hardness of the problem by reducing it into the delay constrained least cost problem. Finally, we propose an approximation latency-aware micro-service mashup approach to solve the problem. Experiment results show that the proposed approach achieves a substantial reduction in network resource consumption while still ensuring the latency constraint. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is employed to gather real-time traffic information for drivers, and base stations in 5G systems are used to assist in traffic data transmission. For rapid implementation, the applications in vehicles are available to be offloaded to edge nodes (ENs) which are enhanced from micro base stations. Despite the benefits of IoV and ENs, the explosive growth of offloaded vehicle applications exceeds the capacity of ENs, causing the overload of fractional ENs. Therefore, it is necessary to offload the computing applications in overloaded ENs to other idle ENs, while it is a challenge to select appropriate offloading destination ENs. In this paper, we first consider edge computing framework for computation offloading in IoV under the architecture of 5G networks. We then formulate a multi-objective optimization problem to select suitable destination ENs, which aims to minimize the vehicle application offloading delay and offloading cost as well as realizing the load balance of ENs. Moreover, a computation offloading method for IoV, named COV, is designed to solve the multi-objective optimization problem. Finally, various simulation analyses demonstrate the effectiveness and efficiency of COV. [ABSTRACT FROM AUTHOR]

Nowadays, due to the advances in mobile and wireless communication, mobile devices are widely used in our daily life. Meanwhile, in the mobile devices, there exits diverse applications which are developed to satisfy the various requirements of mobile users. Correspondingly, a large number of services are produced by the mobile devices. Since the mobile devices have limitations on the battery capacity, physical size, etc., they can hardly complete all the services. To relieve this problem, driven by edge computing, the central units (CUs) in fifth-generation wireless systems (5G) could be enhanced into edge nodes (ENs) for processing. However, during the transmission of edge services, the privacy leakage may occur, and the overall performance of the networks needs to be taken into consideration. In this paper, an optimization problem is defined to improve the resource utilization and load balance for all the ENs while protecting the privacy information and satisfying the time requirement. Then, a balanced service offloading method, abbreviated BSOM, is proposed. Finally, abundant experiments and evaluations are conducted to validate our proposed method is both effective and feasible. [ABSTRACT FROM AUTHOR]