Delay Optimization for Mobile Cloud Computing Application Offloading in Smart Cities
In smart cites, more and more smart mobile devices (SMDs) have many computation-intensive applications to be processed. Mobile cloud computing (MCC) as an effective technology can help SMDs reduce their energy consumption and processing delay by offloading the tasks on the distributed cloudlet. However, due to long transmission delay resulting from the unstable wireless environment, the SMD may be out of the serving area before the cloudlet transmits responses to the user. Thus, delay is a crucial problem for the MCC offloading. In this paper, we consider a multi-SMDs MCC system, where each SMD having an application to be offloaded asks for computation offloading to a cloudlet. In order to minimize the total delay of the SMDs in the system, we jointly take the offloading cloudlet selection, wireless access selection, and computation resource allocation into consideration. We formulate the total delay minimization problem as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. We propose an improved genetic algorithm to obtain a local optimal result. Simulation results demonstrated that our proposal could effectively reduce the system delay.
This paper is supported by the National Key Project under Grant NO. 2017 ZX03001009.
- 5.Mukherjee, A., Gupta, P., De, D.: Mobile cloud computing based energy efficient offloading strategies for femtocell network. In: 2014 Applications and Innovations in Mobile Computing (AIMoC), pp. 28–35, February 2014Google Scholar
- 6.Shu, P., Liu, F., Jin, H., Chen, M., Wen, F., Qu, Y., Li, B.: eTime: energy-efficient transmission between cloud and mobile devices. In: 2013 Proceedings IEEE INFOCOM, pp. 195–199, April 2013Google Scholar
- 7.Liu, D., Khoukhi, L., Hafid, A.S.: Data offloading in mobile cloud computing: a Markov decision process approach. In: IEEE ICC (2017)Google Scholar
- 8.Wang, J., Peng, J., Wei, Y., Liu, D., Fu, J.: Adaptive application offloading decision and transmission scheduling for mobile cloud computing. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–7, May 2016Google Scholar
- 9.Mazza, D., Tarchi, D., Corazza, G.E.: A cluster based computation offloading technique for mobile cloud computing in smart cities. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6, May 2016Google Scholar
- 10.Chen, M.H., Liang, B., Dong, M.: Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp. 1–9 (2017)Google Scholar
- 11.Guo, S., Xiao, B., Yang, Y., Yang, Y.: Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing. In: IEEE INFOCOM 2016 - the IEEE International Conference on Computer Communications, pp. 1–9 (2016)Google Scholar
- 12.Miettinen, A.P., Nurminen, J.K.: Energy efficiency of mobile clients in cloud computing. In: Usenix Conference on Hot Topics in Cloud Computing, p. 4 (2010)Google Scholar
- 13.Genetic Algorithm. https://en.wikipedia.org/wiki/Genetic_algorithm. Accessed 30 Nov 2017
- 14.Rai, A., Bhagwan, R., Guha, S.: Generalized resource allocation for the cloud. In: Proceedings of 3rd ACM Symposium on Cloud Computing, San Jose, CA, USA, pp. 1–12, October 2012Google Scholar
- 15.Weise, T.: Global Optimization Algorithms Theory and Application. http://www.it-weise.de/projects/book.pdf. Accessed 30 Nov 2017