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.
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References
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)
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)
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)
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_15
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)
ISI: Pegasus Project. https://confluence.pegasus.isi.edu (2018). Accessed 26 Aug 2018
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)
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)
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)
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)
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)
Meena, J., Kumar, M., Vardhan, M.: Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access 4, 5065–5082 (2016)
Microsoft: RSSI. https://msdn.microsoft.com/en-us/library/windows/desktop/ms706828%28v=vs.85%29.aspx (2018). Accessed 26 Aug 2018
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)
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)
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)
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)
Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Stanford-CVGL: Stanford Drone Dataset. http://cvgl.stanford.edu/projects/uav_data/ (2018). Accessed 26 Aug 2018
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)
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)
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)
Zeng, H., Cheung, Y.M.: A new feature selection method for Gaussian mixture clustering. Pattern Recogn. 42(2), 243–250 (2009)
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)
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Peng, Q., He, Q., Xia, Y., Wu, C., Wang, S. (2019). Collaborative Workflow Scheduling over MANET, a User Position Prediction-Based Approach. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_3
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DOI: https://doi.org/10.1007/978-3-030-12981-1_3
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