Abstract
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.
Highlights
With the blossom of Internet of Things (IoT) [1, 2], we need to process a huge amount of data deriving from various applications like real-time monitoring [3]
The simulation results show that the proposed intelligent adaptive algorithm can achieve superior performance in terms of weighted reward, service latency, and power consumption
We calculate the average values in all the periods of one episode, including weighted reward, data size of the remaining task, and power consumption of user device
Summary
With the blossom of Internet of Things (IoT) [1, 2], we need to process a huge amount of data deriving from various applications like real-time monitoring [3]. The cloud computing (CC) technology and service migration approach are proposed for enabling users to utilize powerful cloud servers which can effortlessly achieve service function with high performance [4] It would result in heavy traffic burden over the transmission network and large responding latency, since the cloud servers are far away from end users. Considering the data size of remaining task, the power consumption of user device [9, 10], and the appended workload of participant server [11], this paper designs an algorithm for deriving the intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. We propose an ML-based algorithm to derive intelligent adaptive strategies for improving the quality of service, relieving the restrained resource of user device, and balancing the workload of servers.
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