Abstract
Smart mobile phones are vital to the Mobile Cloud Computing (MCC) paradigm where compute jobs can be offloaded to the devices from the Cloud and vice-versa, or the devices can act as peers to collaboratively perform a task. Recent research in IoT context also points to the use of smartphones as sensor gateways highlighting the importance of data processing at the network edge. In either case, when a smart phone is used as a compute resource or a sensor gateway, the corresponding tasks must be executed in addition to the user’s normal activities on the device without affecting the user experience. In this paper, we propose a framework that can act as an enabler of such features by classifying the availability of system resources like CPU, memory, network usage based on applications running on an Android phone. We show that, such app-based classifications are user-specific and app usage varies with different handsets, leading to different classifications. We further show that irrespective of such variation in classification, distinct patterns exist for all users with available opportunity to schedule external tasks, without affecting user experience. Based on the next to-be-used applications, we output a predicted set of system resources. The resource levels along with handset architecture may be used to estimate worst case execution time for external jobs.
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Notes
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RMSE: Root mean-squared error, MAE: Mean absolute error, RRSE: Root relative squared error, RAE: Relative absolute error.
References
Marinelli, E.E.: Hyrax: Cloud Computing on Mobile Devices using MapReduce (2009)
Shi, C., Ammar, M.H. Zegura, E.W., Naik, M.: Computing in cirrus clouds: the challenge of intermittent connectivity. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC 2012, New York, NY, USA, pp. 23–28. ACM (2012)
Shi, C., Lakafosis, V., Ammar, M.H., Zegura, E.W.: Serendipity: enabling remote computing among intermittently connected mobile devices. In: Proceedings of the 13th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2012, New York, NY, USA, pp. 145–154. ACM (2012)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S:. Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC 2012, New York, NY, USA, pp. 13–16. ACM (2012)
Mukherjee, A., Paul, H.S., Dey, S., Banerjee, A.: Angels for distributed analytics in IoT. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 565–570. IEEE (2014)
Adeel, U., Yang, S., McCann, J.A.: Self-optimizing citizen-centric mobile urban sensing systems. In: 11th International Conference on Autonomic Computing (ICAC 2014), Philadelphia, PA, pp. 16–1167. USENIX Association, June 2014
Yang, S., Adeel, U., McCann, J.: Selfish mules: social profit maximization in sparse sensornets using rationally-selfish human relays. IEEE J. Sele. Areas Commun. 31, 1124–1134 (2013)
Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys 2010, New York, NY, USA, pp. 179–194. ACM (2010)
Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., Venkataraman, S.: Identifying diverse usage behaviors of smartphone apps. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC 2011, New York, NY, USA, pp. 329–344. ACM (2011)
An EEMBC Benchmark for Android Devices. http://www.eembc.org/andebench/
Zefferer, T., Teufl, P., Derler, D., Potzmader, K., Oprisnik, A., Gasparitz, H., Hoeller, A.: Power Consumption-based Application Classification and Malware Detection on Android Using Machine-Learning Techniques (2009)
Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P.G.: On the automatic categorisation of android applications. In: CCNC, pp. 149–153. IEEE (2012)
Shabtai, A., Fledel, Y., Elovici, Y.: Automated static code analysis for classifying android applications using machine learning. In: 2010 International Conference on Computational Intelligence and Security (CIS), pp. 329–333, December 2010
Trepn Profiler. https://developer.qualcomm.com/mobile-development/increase-app-performance/trepn-profiler
MacQueen, J.: Some methods for classification and analysis of multivariate observations (1967)
Number of Android applications. http://www.appbrain.com/stats/number-of-android-apps
Shin, C., Hong, J.-H., Dey, A.K.: Understanding and prediction of mobile application usage for smart phones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, New York, NY, USA, pp. 173–182. ACM (2012)
Garner, S.R.: Weka: The waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference, pp. 57–64 (1995)
Weka-for-Android. https://github.com/rjmarsan/Weka-for-Android
Libsvm-androidjni. https://github.com/cnbuff410/Libsvm-androidjni
Ling, C.X., Huang, J., Zhang, H.: AUC: a better measure than accuracy in comparing learning algorithms. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 329–341. Springer, Heidelberg (2003)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2005)
Hand, D.J.: Measuring classifier performance: A coherent alternative to the area under the roc curve. Mach. Learn. 77, 103–123 (2009)
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Mukherjee, A., Basu, A., Dey, S., Datta, P., Paul, H.S. (2015). To Run or Not to Run: Predicting Resource Usage Pattern in a Smartphone. In: Giaffreda, R., et al. Internet of Things. User-Centric IoT. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-19656-5_49
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DOI: https://doi.org/10.1007/978-3-319-19656-5_49
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