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.

The advent of IoT caused vigorous development of multiple services in diverse industry areas. Especially, smart home services have high accessibility than other services since they are based on everyday home appliances, so they gained special attention of application developers. Those services generate a large volume of data: it not only helps service providers to provide services more efficiently, but also helps them to guarantee Service Level Agreement (SLA) of those services. In this paper, we apply time dependency and usage continuity of daily appliances, and correlation between different services to model behavior patterns of service users and generate reliable synthetic data based on their real log data.

In this paper, we tried to enable a mobile device as a resource to access to the grid network. By improved Internet technique, the use of mobile devices is rapidly increased. And some researches in grid computing tried to combine grid computing with mobile devices. However, according to properties of mobile environments, mobile devices have many considerations, such as mobility management, disconnected operation, device heterogeneity, service discovery, resource sharing, security and so on. To conquest these problems, there are two trends for mobile grid computing: a proxy-based mobile grid architecture and an agent-based mobile grid architecture. We focused on a proxy-based mobile grid architecture with IP-paging, which easily manage idle mobile devices and grid resource status information. In result, to support mobile grid services, we propose the IP-paging scheme based on the mobile grid computing framework. Moreover, we have discussed some research issues: location tracking using IP-paging, limited power using grid paging cache, and service migration using reserved resource. And, we also propose a mobile proxy and paging agent prototype.