An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks
Xuejing Li, Yajuan Qin, Huachun Zhou, Du Chen +2
AI summary
70% confidenceThe paper proposes a machine learning-based algorithm for load balancing and task scheduling in mobile fog computing networks, aiming to improve quality of service and reduce latency.
Generated by MESSAI extraction pipeline · review against source PDF
Representative MFC — matched on the paper’s system type only, not its reactor or geometry.
Reported parameters
No values extracted from this paper yet.
Open in lab for full controls, parameter editing, and template overlays.
Open in lab →What they did
- System
- MFC
What worked
No outcome metrics extracted yet.
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.
Key findings
- The algorithm considers data size, power consumption, and workload when generating adaptive strategies for load balancing and task scheduling.
- The proposed algorithm is designed for cloud-aware MFC networks with multiple servers and aims to relieve resource constraints on user devices.
- The algorithm is based on machine learning and software-defined networking technology to enable cooperative task execution between user devices and servers.
Keywords
Identifiers
- Journal
- Wireless Communications and Mobile Computing
- Year
- 2020