Research Article
Collaborative Workflow Scheduling over MANET, a User Position Prediction-Based Approach
@INPROCEEDINGS{10.1007/978-3-030-12981-1_3, author={Qinglan Peng and Qiang He and Yunni Xia and Chunrong Wu and Shu Wang}, title={Collaborative Workflow Scheduling over MANET, a User Position Prediction-Based Approach}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Workflow scheduling Mobile computing Quality-of-service Reliability}, doi={10.1007/978-3-030-12981-1_3} }- Qinglan Peng
Qiang He
Yunni Xia
Chunrong Wu
Shu Wang
Year: 2019
Collaborative Workflow Scheduling over MANET, a User Position Prediction-Based Approach
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_3
Abstract
The explosive increase of mobile devices and advanced communication technologies prompt the emergence of mobile computing. In this paradigm, mobile users’ idle resources can be shared as service through device-to-device links to other users. Some complex workflow-based mobile applications are therefor no longer need to be offloaded to remote cloud, on the contrary, they can be solved locally with the help of other devices in a collaborative way. Nevertheless, various challenges, especially the reliability and quality-of-service of such a collaborative workflow scheduling problem, are yet to be properly tackled. Most studies and related scheduling strategies assume that mobile users are fully stable and with constantly available. However, this is not realistic in most real-world scenarios where mobile users are mobile most of time. The mobility of mobile users impact the reliability of corresponding shared resources and consequently impact the success rate of workflows. In this paper, we propose a reliability-aware mobile workflow scheduling approach based on prediction of mobile users’ positions. We model the scheduling problem as a multi-objective optimization problem and develop an evolutionary multi-objective optimization based algorithm to solve it. Extensive case studies are performed based on a real-world mobile users’ trajectory dataset and show that our proposed approach significantly outperforms traditional approaches in term of workflow success rate.