Energy-and-Time-Saving Task Scheduling Based on Improved Genetic Algorithm in Mobile Cloud Computing
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.
KeywordsMobile cloud computing 5G Task scheduling Genetic algorithm Energy consumption
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.
- 2.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)Google Scholar
- 3.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)Google Scholar
- 5.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)Google Scholar
- 6.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)Google Scholar
- 12.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 CrossRefGoogle Scholar