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Collaborative Workflow Scheduling over MANET, a User Position Prediction-Based Approach

  • Qinglan Peng
  • Qiang He
  • Yunni XiaEmail author
  • Chunrong Wu
  • Shu Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

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.

Keywords

Workflow scheduling Mobile computing Quality-of-service Reliability 

References

  1. 1.
    Abrishami, S., Naghibzadeh, M., Epema, D.H.: Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener. Comput. Syst. 29(1), 158–169 (2013)CrossRefGoogle Scholar
  2. 2.
    Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–971 (2016)Google Scholar
  3. 3.
    Balasubramanian, N., Balasubramanian, A., Venkataramani, A.: Energy consumption in mobile phones: a measurement study and implications for network applications. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, pp. 280–293. ACM (2009)Google Scholar
  4. 4.
    Deb, K.: Multi-objective optimization. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 403–449. Springer, Boston (2014).  https://doi.org/10.1007/978-1-4614-6940-7_15CrossRefGoogle Scholar
  5. 5.
    Giordano, S., Puccinelli, D.: The human element as the key enabler of pervasiveness. In: The 10th IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net) 2011, pp. 150–156. IEEE (2011)Google Scholar
  6. 6.
    ISI: Pegasus Project. https://confluence.pegasus.isi.edu (2018). Accessed 26 Aug 2018
  7. 7.
    Kharbash, S., Wang, W.: Computing two-terminal reliability in mobile ad hoc networks. In: Wireless Communications and Networking Conference 2007, WCNC 2007. pp. 2831–2836. IEEE (2007)Google Scholar
  8. 8.
    Li, W., Xia, Y., Zhou, M., Sun, X., Zhu, Q.: Fluctuation-aware and predictive workflow scheduling in cost-effective infrastructure-as-a-service clouds. IEEE Access 6, 61488–61502 (2018)CrossRefGoogle Scholar
  9. 9.
    Liu, S., Cao, H., Li, L., Zhou, M.: Predicting stay time of mobile users with contextual information. IEEE Trans. Autom. Sci. Eng. 10(4), 1026–1036 (2013)CrossRefGoogle Scholar
  10. 10.
    Maheswaran, M., Ali, S., Siegal, H., Hensgen, D., Freund, R.F.: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: Proceedings of the Eighth Heterogeneous Computing Workshop 1999, (HCW 1999), pp. 30–44. IEEE (1999)Google Scholar
  11. 11.
    Mao, M., Humphrey, M.: Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–12. IEEE (2011)Google Scholar
  12. 12.
    Meena, J., Kumar, M., Vardhan, M.: Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access 4, 5065–5082 (2016)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Qiao, S., Han, N., Zhu, W., Gutierrez, L.A.: TraPlan: an effective three-in-one trajectory-prediction model in transportation networks. IEEE Trans. Intell. Transp. Syst. 16(3), 1188–1198 (2015)CrossRefGoogle Scholar
  15. 15.
    Rodriguez, M.A., Buyya, R.: Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)CrossRefGoogle Scholar
  16. 16.
    Sakellariou, R., Zhao, H.: A hybrid heuristic for DAG scheduling on heterogeneous systems. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium 2004, p. 111. IEEE (2004)Google Scholar
  17. 17.
    Schad, J., Dittrich, J., Quiané-Ruiz, J.A.: Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proc. VLDB Endow. 3(1–2), 460–471 (2010)CrossRefGoogle Scholar
  18. 18.
    Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Stanford-CVGL: Stanford Drone Dataset. http://cvgl.stanford.edu/projects/uav_data/ (2018). Accessed 26 Aug 2018
  20. 20.
    Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)CrossRefGoogle Scholar
  21. 21.
    Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y., Wen, J.: Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 28(12), 3401–3412 (2017)CrossRefGoogle Scholar
  22. 22.
    Xia, Y., Zhou, M., Luo, X., Pang, S., Zhu, Q.: Stochastic modeling and performance analysis of migration-enabled and error-prone clouds. IEEE Trans. Ind. Inf. 11(2), 495–504 (2015)CrossRefGoogle Scholar
  23. 23.
    Zeng, H., Cheung, Y.M.: A new feature selection method for Gaussian mixture clustering. Pattern Recogn. 42(2), 243–250 (2009)CrossRefGoogle Scholar
  24. 24.
    Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Qinglan Peng
    • 1
  • Qiang He
    • 2
  • Yunni Xia
    • 1
    Email author
  • Chunrong Wu
    • 1
  • Shu Wang
    • 3
  1. 1.Software Theory and Technology Chongqing Key LabChongqing UniversityChongqingChina
  2. 2.School of Software and Electrical EngineeringSwiburne University of TechnologyMelbourneAustralia
  3. 3.School of information, Liaoning UniversityShenyangChina

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