Abstract
Along With vast deployment of mobile cloud computing systems, users accessing any information on the Internet by smart phones are often based on continuous data communication. However, when the communication status is unstable, the mobile client needs to establish multiple connections with the cloud. This leads to great energy consumption, which poses a huge challenge to the usability of mobile cloud computing systems. In this article, considering the similarity of the accessibility data of strong interactive users and the predictability of user behaviour data, we proposes a link prediction method based on the maximization of user interaction behaviour (Maximize Interaction Link Prediction) in a specific environment for the mobile cloud computing: First, based on the data prediction model, we use the interaction degree method to improve the access data prediction for known users; Secondly, combining with the social network method we analyze and filter the prediction data; At last, we pre-store the above prediction data by the pre-storage mechanism. The Evaluations show that it can reduce mobile energy consumption significantly by around 20%.
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References
Burgstahler, D., Lampe, U., Richerzhagen, N., Steinmetz, R.: Push vs. pull: an energy perspective (short paper), pp. 190–193 (2013)
Eom, B., Lee, C., Lee, H., Ryu, W.: An adaptive remote display scheme to deliver mobile cloud services. IEEE Trans. Consum. Electron. 60(3), 540–547 (2014)
Khairy, A., Ammar, H.H., Bahgat, R.: Smartphone energizer: extending smartphone’s battery life with smart offloading, pp. 329–336 (2013)
Kumar, K., Lu, Y.: Cloud computing for mobile users: can offloading computation save energy? IEEE Comput. 43(4), 51–56 (2010)
Liu, F., Shu, P., Lui, J.C.S.: Appatp : an energy conserving adaptive mobile-cloud transmission protocol. IEEE Trans. Comput. 64(11), 3051–3063 (2015)
Misra, A., Lim, L.: Optimizing sensor data acquisition for energy-efficient smartphone-based continuous event processing, vol. 1, pp. 88–97 (2011)
Paterna, F., Acquaviva, A., Benini, L.: Aging-aware energy-efficient workload allocation for mobile multimedia platforms. IEEE Trans. Parallel Distrib. Syst. 24(8), 1489–1499 (2013)
Pedersen, P.E.: Adoption of mobile internet services: an exploratory study of mobile commerce early adopters. J. Organ. Comput. Electron. Commer. 15(3), 203–222 (2005)
Rudenko, A., Reiher, P.L., Popek, G.J., Kuenning, G.H.: Saving portable computer battery power through remote process execution. Mob. Comput. Commun. Rev. 2(1), 19–26 (1998)
Shen, H., Kumar, M., Das, S.K., Wang, Z.: Energy-efficient data caching and prefetching for mobile devices based on utility. Mob. Netw. Appl. 10(4), 475–486 (2005)
Xu, Y., Mao, S.: A survey of mobile cloud computing for rich media applications. IEEE Wirel. Commun. 20(3), 46–53 (2013)
Zhou, L., Yang, Z., Rodrigues, J.J.P.C., Guizani, M.: Exploring blind online scheduling for mobile cloud multimedia services. IEEE Wirel. Commun. 20(3), 54–61 (2013)
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Xu, J., Guan, C., Xu, X. (2019). Energy-Efficiency for Smartphones Using Interaction Link Prediction in Mobile Cloud Computing. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_39
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DOI: https://doi.org/10.1007/978-981-13-3044-5_39
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