A Review of Computational Task Offloading Approaches in Mobile Computing

Authors(2) :-Krutika Bhandwalkar, Prof. Rahul Shahane

Mobile cloud computing permits the execution of calculation escalated uses of cell phones in computational clouds, and this procedure of executing in cloud by sending the application VM/Components is called application/code/part offloading. Offloading is a successful strategy to spare the execution time and vitality utilization of cell phones. In this way it amplifies the battery life of cell phones. Applications are first apportioned into offloadable and on-offloadable segments, which are then exchanged to remote server for execution. We concentrate the booking of computational assignments on one nearby processor and one remote processor with correspondence delay. This issue has vital application in cloud computing. In spite of the fact that the correspondence time to transmit an errand can be induced from the known information size of the assignment and the transmission data transfer capacity, the preparing time of the undertaking is for the most part obscure until it is handled to completion. The target of this paper is to investigate the distinctive systems of offloading and application dividing techniques. These strategies are completely surveyed in this paper. This paper likewise highlights the examination of various systems on the premise of their commitment, benefits, negative marks and furthermore on the premise of change in execution time, vitality utilization, correspondence time.

Authors and Affiliations

Krutika Bhandwalkar
Department of Computer Science and Engineering, Wainganga College of Engineering & Management, Nagpur, Maharashtra, India
Prof. Rahul Shahane
Department of Computer Science and Engineering, Wainganga College of Engineering & Management, Nagpur, Maharashtra, India

Computational offloading, mobile cloud computing, computation with communication, semi-online algorithms

  1. M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, pp. 5058, Apr. 2010.
  2. R. L. Graham, “Bounds for certain multiprocessing anomalies,” Bell System Technical Journal, vol. 45, pp. 15631541, 1966.
  3. D. B. Shmoys, J. Wein, and D. P. Williamson, “Scheduling parallel machines on-line,” SIAM J. Comput., vol. 24, no. 6, pp. 13131331, Dec. 1995.
  4. G. Fries, “Scheduling independent tasks on uniform processors,” SIAM J. Computing, vol. 13, no. 1, pp. 705716, 1984.
  5. A. Kovcs, “New approximation bounds for lpt scheduling.” Algo- rithmica, vol. 57, no. 2, pp. 413433, 2010.
  6. R. L. Graham, E. L. Lawler, J. K. Lenstra, and A. H. G. Rinnooy Kan, “Optimization and approximation in deterministic sequencing and scheduling: a survey,” Annals of discrete mathematics, vol. 5, no. 2, pp. 287326, 1979.
  7. D. P. Williamson and D. B. Shmoys, The Design of Approximation Algorithms, 1st ed. Cambridge University Press, 2011.
  8. H. Casanova, A. Legrand, D. Zagorodnov, and F. Berman, “Heuristics for scheduling parameter sweep applications in grid environments,” in Heterogeneous Computing Workshop, 2000, pp. 349363.
  9. A. Giersch, Y. Robert, and F. Vivien, “Scheduling tasks sharing files on heterogeneous master-slave platforms,” Journal of Systems Architecture, vol. 52, no. 2, pp. 88104, 2006.
  10. K. Kaya and C. Aykanat, “Iterative-improvement-based heuristics for adaptive scheduling of tasks sharing files on heterogeneous master-slave environments.” IEEE Trans. Parallel Distrib. Syst., vol. 17, no. 8, pp. 883896, 2006.
  11. O. Beaumont, A. Legrand, and Y. Robert, “The master-slave paradigm with heterogeneous processors,” IEEE Trans. Parallel Distrib. Syst., vol. 14, no. 9, pp. 897908, 2003.
  12. M. Drozdowski, Scheduling for Parallel Processing, 1st ed. Springer Publishing Company, Incorporated, 2009.
  13. K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava, “A survey of computation offloading for mobile systems,” Mob. Netw. Appl., vol. 18, no. 1, pp. 129140, Feb. 2013.
  14. E. K. Tabak, B. B. Cambazoglu, and C. Aykanat, “Improving the performance of independent task assignment heuristics minmin, maxmin and sufferage,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 5, pp. 12441256, May 2014.
  15. V. V. Vazirani, Approximation Algorithms. New York, NY, USA: Springer-Verlag New York, Inc., 2001. 12
  16. H. Kellerer, V. Kotov, M. G. Speranza, and Z. Tuza, “Semi on-line algorithms for the partition problem,” Operations Research Letters, vol. 21, no. 5, pp. 235 242, 1997.
  17. T. E. Cheng, H. Kellerer, and V. Kotov, “Semi-on-line multiprocessor scheduling with given total processing time,” Theoretical Computer Science, vol. 337, no. 13, pp. 134 146, 2005.
  18. S. Albers and M. Hellwig, “Semi-online scheduling revisited,” Theoretical Computer Science, vol. 443, pp. 1 9, 2012.
  19. H. Kellerer, V. Kotov, and M. Gabay, “An efficient algorithm for semi-online multiprocessor scheduling with given total processing time,” Journal of Scheduling, vol. 8, no. 6, pp. 623630, 2015.
  20. C. Ng, Z. Tan, Y. He, and T. Cheng, “Two semi-online scheduling problems on two uniform machines,” Theoretical Computer Science, vol. 410, no. 810, pp. 776 792, 2009.
  21. R. K. Balan, M. Satyanarayanan, S. Y. Park, and T. Okoshi, “Tactics-based remote execution for mobile computing,” in Proc. International Conference on Mobile Systems, Applications, and Services (MobiSys), 2003, pp. 273286.
  22. M. Chen, B. Liang, and M. Dong, “A Semidefinite Relaxation Approach to Mobile Cloud Offloading with Computing Access Point,” pp. 15.
  23. F. Xia, F. Ding, J. Li, X. Kong, L. T. Yang, and J. Ma, “Phone2Cloud : Exploiting computation offloading for energy saving on smartphones in mobile cloud computing,” pp. 95111, 2014.
  24. K. Wolter, H. Wu, Q. Wang, and K. Wolter, “Tradeoff between Performance Improvement and Energy Saving in Mobile Cloud Offloading Systems Tradeoff between Performance Improvement and Energy Saving in Mobile Cloud Offloading Systems,” no. JUNE, 2013.
  25. Z. Jiang, S. Member, S. Mao, and S. Member, “Energy Delay Tradeoff in Cloud Offloading for Multi-Core Mobile Devices,” vol. 3, 2015.
  26. A. Ashok, P. Steenkiste, and F. Bai, “Enabling Vehicular Applications using Cloud Services through Adaptive Computation Offloading,” 2015.
  27. H. Wu, Q. Wang, and K. Wolter, “Mobile Healthcare Systems with Multi-cloud Offloading,” 2013.
  28. I. Technologies, “Overview of Offloading in Smart Mobile Devices for Mobile Cloud Computing,” vol. 5, no. 6, pp. 78557860, 2014.
  29. B. Zhou, A. V. Dastjerdi, R. N. Calheiros, S. N. Srirama, and R. Buyya, “A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service,” 2015.
  30. K. Kumar and J. Liu, “A Survey of Computation Offloading for Mobile Systems,” 2012.
  31. Y. Wang, I. Chen, V. Tech, and D. Wang, “A Survey of Mobile Cloud Computing Applications : Perspectives and Challenges,” pp. 129.

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 525-530
Manuscript Number : CSEIT172356
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Krutika Bhandwalkar, Prof. Rahul Shahane, "A Review of Computational Task Offloading Approaches in Mobile Computing", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.525-530, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT172356

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