International Journal of Computer Networks and Applications

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Optimization of Smart Mobile Device Work Time Using an Optimal Decision Tree Classifier and Data Caching Technique in on Premise Network


Affiliations
1 Department of Computer Science and Engineering, Ballari Institute of Technology and Management, Ballari, Karnataka, India
2 Department of Information Science and Engineering, RajaRajeswari College of Engineering, Bengaluru, Karnataka, India
 

Today, Most smart mobile devices are facilitated with advanced processing hardware and short-range data communication systems by which they are practically capable to provide effective execution services to the neighbor mobile device client request and/or receive services on a need basis within the local area network. Therefore, to relish the powerful capability of these smart mobile devices in the private campus network, we propose an intelligent composite offload decision algorithm (ICODA) framework that attempts to connect several smart mobile devices in wireless local area network and make them apply intelligence before servicing each other request preferably without the internet. The significance of the proposed framework is that it has a mechanism to make a data offloading decision using an optimal decision tree classifier model and also a mechanism to avoid data offloading operation using the data cache neural networks model. The experimental results obtained are obvious to show the minimal client system battery utilization and hence an optimized work time for a smart mobile client device that participates in the ICODA framework.

Keywords

Private Network, Client-Side Local Cache, Device Status Report Generation, Data Offload Decision, Server Side Global Cache, Average Battery Energy and Task Run Time Measure, Optimized Work Time.
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  • Silva, Joaquim & Marques, Eduardo & Lopes, Luís & Silva, Fernando, “Energy-aware adaptive offloading of soft real-time jobs in mobile edge clouds”, Journal of Cloud Computing, volume 10, Pages 1-21,2021.

  • Wang, Yantong; Friderikos, Vasilis, "A Survey of Deep Learning for Data Caching in Edge Network" Informatics , volume 7, no. 4, Pages 1-29, 2020.

  • Goncalo Carvalho, Bruno Cabral, Vasco Pereira, Jorge Bernardino, “Computation offloading in Edge Computing environments using Artificial Intelligence techniques”, Engineering Applications of Artificial Intelligence, Volume 95, 2020.

  • Sanches P., Silva J.A., Teofilo A., Paulino H, “Data-Centric Distributed Computing on Networks of Mobile Devices”, Euro-Par 2020: Parallel Processing - Lecture Notes in Computer Science, volume 12247. Springer, Pages 296-311, 2020.

  • T. Q. Dinh, Q. D. La, T. Q. S. Quek and H. Shin, "Learning for Computation Offloading in Mobile Edge Computing," in IEEE Transactions on Communications, vol. 66, no. 12, pp. 6353-6367, Dec. 2018.

  • Z. Wen, K. Yang, X. Liu, S. Li and J. Zou, "Joint Offloading and Computing Design in Wireless Powered Mobile-Edge Computing Systems With Full-Duplex Relaying," in IEEE Access, vol. 6, pp. 72786-72795, 2018.

  • Z. Chang, L. Lei, Z. Zhou, S. Mao and T. Ristaniemi, "Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era," in IEEE Wireless Communications, vol. 25, no. 3, pp. 28-35, June 2018.

  • Farhan Azmat Ali, Pieter Simoens, Tim Verbelen, Piet Demeester, B Dhoedt, "Mobile device power models for energy-efficient dynamic offloading at runtime", Journal of Systems and Software, Volume 113, PP. 173-187, 2016.

  • N. Fernando, S. W. Loke and W. Rahayu, "Computing with Nearby Mobile Devices: A Work Sharing Algorithm for Mobile Edge-Clouds," in IEEE Transactions on Cloud Computing, vol. 7, no. 2, pp. 329-343, 1 April-June 2019.

  • S. Yu, R. Langar, X. Fu, L. Wang and Z. Han, "Computation Offloading With Data Caching Enhancement for Mobile Edge Computing," in IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 11098-11112, 2018.

  • J. L. D. Neto, S. Yu, D. F. Macedo, J. M. S. Nogueira, R. Langar and S. Secci, "ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing," in IEEE Transactions on Mobile Computing, vol. 17, no. 11, pp. 2660-2674, 2018.

  • R. Aldmour, S. Yousef, M. Yaghi and G. Kapogiannis, "MECCA offloading cloud model over wireless interfaces for optimal power reduction and processing time," 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation pp. 1-8, 2017.

  • S. Ahn, J. Lee, S. Park, S. H. S. Newaz and J. K. Choi, "Competitive Partial Computation Offloading for Maximizing Energy Efficiency in Mobile Cloud Computing," in IEEE Access, vol. 6, pp. 899-912, 2018.

  • Yiming Miao, Gaoxiang Wu, Miao Li, Ahmed Ghoneim, Mabrook Al-Rakhami, M. Shamim Hossain, “Intelligent task prediction and computation offloading based on mobile-edge cloud computing”, Future Generation Computer Systems, Volume 102, Pages 925-931, 2020.

  • P. Nawrocki, B. Sniezynski, H. Slojewski, “Adaptable mobile cloud computing environment with code transfer based on machine learning”, Pervasive and Mobile Computing, Volume 57, Pages 49-63, 2019.

  • Hyun-Woo Kim, Jong Hyuk Park, Young-Sik Jeong, “Adaptive job allocation scheduler based on usage pattern for computing offloading of IoT”, Future Generation Computer Systems, Volume 98, Pages 18 -24, 2019.

  • Abdulhameed Alelaiwi, “An efficient method of computation offloading in an edge cloud platform”, Journal of Parallel and Distributed Computing, Volume 127, Pages 58-64, 2019.

  • Lee, Hochul ; Lee, Jaehun ; Lee, Young Choon ; Kang, Sooyong, “CollaboRoid : Mobile platform support for collaborative applications”, Pervasive and Mobile Computing, Vol. 55. pp. 13-31, 2019.


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  • Optimization of Smart Mobile Device Work Time Using an Optimal Decision Tree Classifier and Data Caching Technique in on Premise Network

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Authors

Sridhar S. K.
Department of Computer Science and Engineering, Ballari Institute of Technology and Management, Ballari, Karnataka, India
J. Amutharaj
Department of Information Science and Engineering, RajaRajeswari College of Engineering, Bengaluru, Karnataka, India

Abstract


Today, Most smart mobile devices are facilitated with advanced processing hardware and short-range data communication systems by which they are practically capable to provide effective execution services to the neighbor mobile device client request and/or receive services on a need basis within the local area network. Therefore, to relish the powerful capability of these smart mobile devices in the private campus network, we propose an intelligent composite offload decision algorithm (ICODA) framework that attempts to connect several smart mobile devices in wireless local area network and make them apply intelligence before servicing each other request preferably without the internet. The significance of the proposed framework is that it has a mechanism to make a data offloading decision using an optimal decision tree classifier model and also a mechanism to avoid data offloading operation using the data cache neural networks model. The experimental results obtained are obvious to show the minimal client system battery utilization and hence an optimized work time for a smart mobile client device that participates in the ICODA framework.

Keywords


Private Network, Client-Side Local Cache, Device Status Report Generation, Data Offload Decision, Server Side Global Cache, Average Battery Energy and Task Run Time Measure, Optimized Work Time.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F210720