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With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks