As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.
Since improvement of wireless communication, IP based mobility management protocols have been studied to provide seamless communication and mobility management. The vehicular ad hoc network (VANET) is one of mobility management protocols, especially providing seamless connection with inter/intra/inner vehicle communication. However, each vehicle moves fast that causes short-lived connections with Access Router (AR). Based on vehicles' characteristic, it is hard to provide the availability of IP services in VANET. The most critical issue of the design of scalable routing algorithm is to provide robustness of frequent path disruption caused by vehicles' mobility. In this paper, we pursue the characteristics of vehicles' mobility and analyze them. With the navigation information which is one of vehicles' mobility characteristics, we classify the mobility into intrahighway mobility and global mobility management. Furthermore, we propose mobility management scheme based on route prediction in VANET. Handoffs with intrahighway mobility are managed locally and transparency is provided to CHs, while global mobility is managed with Mobile IPv6. Finally, through the numerical analysis, we show that proposed mobility management protocol reduces handoff latency, signaling costs, and packet loss.
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