Abstract
With the collaboration of 5G network and mobile cloud computing(MCC), mobile devices can be offered important opportunities and new challenges in terms of energy saving and performance enhancement, sophisticated applications running on smart phones, which are called tasks in MCC environment, may be same or different. The paper studies the problem of task scheduling in MCC. Firstly, a task-virtual machine (VM) assignment strategy is presented; Secondly, on the basis of the strategy, we improve genetic algorithm (GA) which uses grouping multi-level encoding and dual fitness function (GMLE-DFF), GMLE means that the individual adopts hierarchical coding according to VMs grouping and tasks queuing. DFF refers to the reasonable combination of the optimal time span and the maximum resources utilization and minimum opened number of VMs. By simulating and realizing traditional GA, Sufferage algorithm and our improved GA, the results show the improved GA is superior to other two algorithms for reducing energy consumption while the task completion time is satisfied.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, F., Shu, P., et al.: Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. Wirel. Commun. IEEE 20(3), 14–22 (2013)
KołOdziej, J., Xhafa, F.: Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids. Int. J. Appl. Math. Comput. Sci. 21(2), 243–257 (2011)
Li, Z.-Y., Chen, S.-M., Yang, B., et al.: Multi-objective memetic algorithm for task scheduling on heterogeneous cloud. Chin. J. Comput. 2016(2)
Li, J., Qiu, M., Ming, Z., et al.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)
Guo, L., Zhao, S., Shen, S., et al.: Task scheduling optimization in cloud computing based on heuristic algorithm. J. Netw. 7(3), 547–553 (2012)
Li, J., Qiu, M., Niu, J., et al.: Feedback dynamic algorithms for preemptable job scheduling in cloud systems. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 561–564. IEEE (2010)
Taheri, J., Lee, Y.C., Zomaya, A.Y., et al.: A Bee Colony based optimization approach for simultaneous job scheduling and data replication in grid environments. Comput. Oper. Res. 40(6), 1564–1578 (2013)
Lin, X., Wang, Y., Xie, Q., et al.: Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans. Serv. Comput. 8(2), 175–186 (2015)
Kumar, N., et al.: Performance analysis of Bayesian coalition game-based energy-aware virtual machine migration in vehicular mobile cloud. Netw. IEEE 29(2), 62–69 (2015)
Zomaya, A.Y., Teh, Y.H.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans. Parallel Distrib. Syst. 12(9), 899–911 (2001)
Thede, S.M.: An introduction to genetic algorithms. J. Comput. Sci. Coll. 20(1), 115–123 (2004)
Prasad Acharya, G., Asha Rani, M.: Fault-tolerant multi-core system design using pb model and genetic algorithm based task scheduling. In: Satapathy, S.C., Rao, N.B., Kumar, S.S., Raj, C.D., Rao, V.M., Sarma, G.V.K. (eds.) Microelectronics, Electromagnetics and Telecommunications. LNEE, vol. 372, pp. 449–458. Springer, New Delhi (2016). doi:10.1007/978-81-322-2728-1_41
Li, X., et al.: Service operator-aware trust scheme for resource matchmaking across multiple clouds. IEEE Trans. Parallel Distrib. Syst. 26(5), 1419–1429 (2015)
Funding Acknowledgments
The work is supported by the National Nature Science Foundation of China (No. 61370069, 61672111), Fok Ying Tung Education Foundation (No. 132032), Beijing Natural Science Foundation (No. 4162043), and the Cosponsored Project of Beijing Committee of Education.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Li, J., Li, X., Zhang, R. (2017). Energy-and-Time-Saving Task Scheduling Based on Improved Genetic Algorithm in Mobile Cloud Computing. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-59288-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59287-9
Online ISBN: 978-3-319-59288-6
eBook Packages: Computer ScienceComputer Science (R0)