The International Arab Journal of Information Technology (IAJIT)


Context Aware Mobile Application Pre-Launching Model using KNN Classifier

Mobile applications are the application software which can be executed in mobile devices. The Performance of the mobile application is major factor to be considered while developing the application software. Usually, the user uses a sequence of applications continuously. So, pre-launching of the mobile application is the best methodology used to increase the launch time of the mobile application. In Android Operating System (OS) they use cache policies to increase the launch time. But whenever a new application enters into the cache it removes the existing application from the cache even it is repeatedly used by the user. So the removed application needs to be re-launched again. To rectify it, we suggest K number of applications for pre-launching by calculating the affinity between the applications. Because, the user may uses the set of applications together for more than one time. We discover those applications from the usage pattern based on Launch Delay (LD), Power Consumption (PC), App Affinity, Spatial and Temporal relations and also, a K-Nearest Neighbour (KNN) classifier machine learning algorithm is used to increase the accuracy of prediction.

[1] Albazaz D., “Design A Mini-Operating System for Mobile Phone,” The International Arab Journal of Information Technology, vol. 9, no. 1, pp. 56-65, 2012.

[2] Balasubramanian N., Balasubramanian A., and Venkataramani A., “Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, Chicago, pp. 280-293, 2009.

[3] Chung Y., Lo Y., and King C., “Enhancing User Experiences by Exploiting Energy and Launch Delay Trade-off of Mobile Multimedia Applications,” ACM Transactions on Embedded Computing Systems, vol. 12, no. 1, pp. 1-19, 2013.

[4] Dodge Y., Cox D., and Commenges D., The Oxford Dictionary of Statistical Terms, Oxford University Press on Demand, 2006.

[5] Hssina B., Merbouha A., Ezzikouri H., and Erritali M., “A Comparative Study of Decision Tree ID3 and C4. 5,” International Journal of Advanced Computer Science and Applications, vol. 4, no. 2, pp. 13-19, 2014.

[6] Kim H., Lim H., Manatunga D., Kim H., and Park G., “Accelerating Application Start-Up with Nonvolatile Memory in Android Systems,” IEEE Micro, vol. 35, no. 1, pp. 15-25, 2015.

[7] Kim S., Jeong J., Kim J., and Maeng S., “Smartlmk: A Memory Reclamation Scheme for Improving User-Perceived App Launch Time,” ACM Transactions on Embedded Computing Systems, vol. 15, no. 3, pp. 1-25, 2016.

[8] Li C., Bao J., and Wang H., “Optimizing Low Memory Killers for Mobile Devices Using Reinforcement Learning,” in Proceeding of the 13th International Wireless Communications and Mobile Computing Conference, Valencia, pp. 2169-2174, 2017.

[9] Lin Y., Yang C., Li H., and Wang C., “A Hybrid DRAM/PCM Buffer Cache Architecture for Smartphones with Qos Consideration,” ACM Transactions on Design Automation of Electronic Systems, vol. 22, no. 2, pp. 1-22, 2016.

[10] Lu E. and Yang Y., “Mining Mobile Application Usage Pattern for Demand Prediction By Considering Spatial and Temporal Relations,” 0 20 40 60 80 100 KNNNaïve Bayes Decision Tree Bayes Net Random Tree Accuracy Machine Learing Algorithms Accuracy Context Aware Mobile Application Pre-Launching Model using KNN Classifier 941 GeoInformatica, vol. 22, no. 4, pp. 693-721, 2018.

[11] Malini A., Sundarakantham K., Prathibhan C., and Bhavithrachelvi A., “Fuzzy-based Automated Interruption Testing Model for Mobile Applications,” International Journal of Business Intelligence and Data Mining, vol. 15, no. 2, pp. 228-253, 2019.

[12] Mittal R., Kansal A., and Chandra R., “Empowering Developers to Estimate App Energy Consumption,” in Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, Istanbul, pp. 317- 328, 2012.

[13] Nguyen P. and Garg A., “Application Pre- Launch to Reduce User Interface Latency,” U.S. Patent No. 7,076,616. Washington, DC: U.S. Patent and Trademark Office, 2006.

[14] Parate A., Böhmer M., Chu D., Ganesan D., and Marlin B., “Practical Prediction and Prefetch for Faster Access to Applications on Mobile Phones,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, Zurich, pp. 275-284, 2013.

[15] Schwartz C., Hoßfeld T., Lehrieder F., and Tran- Gia P., “Angry Apps: The Impact of Network Timer Selection on Power Consumption, Signalling Load, and Web Qoe,” Journal of Computer Networks and Communications, 2013.

[16] Shi L., Li J., Jason Xue C., and Zhou X., “Hybrid Nonvolatile Disk Cache for Energy-Efficient and High-Performance Systems,” ACM Transactions on Design Automation of Electronic Systems, vol. 18, no. 1, pp. 1-23, 2013.

[17] Shin C., Hong J., and Dey A., “Understanding and Prediction of Mobile Application Usage for Smart Phones,” in Proceedings of the ACM Conference on Ubiquitous Computing, Pittsburgh, pp. 173-182, 2012.

[18] Song W., Kim Y., Kim H., Lim J., and Kim J., “Personalized Optimization for Android Smartphones,” ACM Transactions on Embedded Computing Systems, vol. 13, no. 2, pp. 1-25, 2014.

[19] Yan T., Chu D., Ganesan D., Kansal A., and Liu J., “Fast App Launching for Mobile Devices Using Predictive User Context,” in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Low Wood Bay, pp. 113-126, 2012.

[20] Zhao Y., Laser M., Lyu Y., and Medvidovic N., “Leveraging Program Analysis to Reduce User- perceived Latency in Mobile Applications,” in Proceedings of the 40th International Conference on Software Engineering, Gothenburg, pp. 176- 186, 2018.

[21] Zhong K., Wang T., Zhu X., Long L., Liu D., Liu W., Shao Z., and Sha E., “Building High- performance Smartphones Via Non-volatile Memory: The Swap Approach,” in Proceeding of the International Conference on Embedded Software, Uttar Pradesh, pp. 1-10, 2014. Malini Alagarsamy obtained her PhD in Information and Communication Engineering from Anna University, Chennai. She is currently working as an assistant professor at Thiagarajar College of Engineering, Madurai, India. She has published several research papers in journals and international/national conferences. Her research interest includes software Engineering, Testing, Mobile Application development, Green Computing, Internet of Things, Block chain and Machine Learning. Ameena Sahubar Sathik obtained her B.E in Computer Science and Engineering from Anna University (University College of Engineering, Ramanathapuram) in 2017 and completed her Master degree (M.E) in Computer Science and Engineering in Anna University in 2019. She is currently working as a Senior Systems Engineer at Infosys Pvt Ltd. Her research interest includes software testing, software design and mobile application development.