Volume 16, Issue 3 (12-2019)                   JSDP 2019, 16(3): 22-3 | Back to browse issues page


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Zahedi S, Yousefi S, Solouk V. Design and Evaluation of a Method for Partitioning and Offloading Web-based Applications in Mobile Systems with Bandwidth Constraints. JSDP 2019; 16 (3) :22-3
URL: http://jsdp.rcisp.ac.ir/article-1-819-en.html
Abstract:   (3173 Views)
Computation offloading is known to be among the effective solutions of running heavy applications on smart mobile devices. However, irregular changes of a mobile data rate have direct impacts on code partitioning when offloading is in progress. It is believed that once a rate-adaptive partitioning performed, the replication of such substantial processes due to bandwidth fluctuation can be avoided. Currently, a wide range of mobile applications are based on web services, which in turn influences the process of offloading and partitioning. As a result, mobile users are prone to face difficulties in data communications due to cost of preferences or connection quality. Taking into account the fluctuations of mobile connection bandwidth and thereby data rate constraints, the current paper proposes a method of adaptive partitioning and computation offloading in three forms. Accordingly, an optimization problem is primarily formulated to each of three main objectives under the investigation. These objectives include run time, energy consumption and the weighted composition of run time and energy consumption. Next, taking into consideration the time complexity of the optimization problems, a heuristic partitioning method based on Genetic Algorithm (GABP) is proposed to solve each of the three objectives and with the capability of acceptable performance maintenance in both dynamic and static partitionings. In order to evaluate and analyze the performance of the proposed approach, a simulation framework was built to run for random graphs of different sizes with the capability of setting specific bandwidth limits as target. The simulation results evidence improved performance against bandwidth fluctuations when compared to similar approaches. Moreover, it was also seen that once the problem circumstances are modified, the offloading can take place in the vicinity of the target node. Furthermore, we implemented the proposed method in form of an application on Android platform to conduct experiments on real applications. The experiments prove that those partitions of the applications requiring higher processing reqources rather than data rate are the best candidates for offloading.
 
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Type of Study: Research | Subject: Paper
Received: 2017/12/15 | Accepted: 2019/06/19 | Published: 2020/01/7 | ePublished: 2020/01/7

References
1. [1] K.Kumar and et al., "A survey of computation offloading for mobile systems", Mobile Networks and Applications, vol. 18(1), p p. 129-140, 2013.
2. [2] X.Ma and et al., "When mobile terminals meet the cloud: computation offloading as the bridge", IEEE Network, vol. 27(5), pp. 28-33, 2013.
3. [3] A.R.Khan and et al., "A survey of mobile cloud computing application models", Communications Surveys & Tutorials, IEEE, vol. 16(1), pp. 393-413, 2014.
4. [4] J.Niu, W. Song, and M. Atiquzzaman, "Band-width-adaptive partitioning for distributed execu-tion optimization of mobile applications", Journal of Network and Computer Applications, vol.37, pp. 334-347. 2014.
5. [5] K.Kumar and Y.-H. Lu, "Cloud computing for mobile users: Can offloading computation save energy?" Computer,vol. 43(4), pp. 51-56. 2010.
6. [6] B.-G.Chun and et al, "Clonecloud: elastic execution between mobile device and cloud", in Proceedings of the sixth conference on Computer systems, ACM, 2011.
7. [7] P. Di Lorenzo, S. Barbarossa, and S. Sardellitti, "Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing", arXiv preprint arXiv:1307, 2013, pp3835.
8. [8] F.Xia and et al, "Phone2Cloud: Exploiting computation offloading for energy saving on smartphones in mobile cloud computing", Information Systems Frontiers, vol. 16(1), pp. 95-111. 2014
9. [9] M.H. Tandel and V.S. Venkitachalam, "Cloud Computing in Smartphone: Is offloading a better-bet?", CS837-F12-MW-04A Wichita State Uni-versity, 2013.
10. [10] T.Verbelen and et al, "Graph partitioning algorithms for optimizing software deployment in mobile cloud computing", Future Generation Computer Systems, vol. 29(2), pp. 451-459. 2013.
11. [11] E.Cuervo and et al , "MAUI: making smart-phones last longer with code offload", in Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 2010.
12. [12] Y.Zhang and et al, "Refactoring android java code for on-demand computation offloading", in ACM SIGPLAN Notices .ACM, 2012.
13. [13] R.Kemp and et al, "Cuckoo: a computation offloading framework for smartphones", in Mobile Computing, Applications, and Services, Springer, pp. 59-79, 2012.
14. [14] D.Kovachev, T. Yu, and R. Klamma, "Adaptive computation offloading from mobile devices into the cloud", in Parallel and Distributed Pro-cessing with Applications (ISPA), IEEE 10th International Symposium on, 2012.
15. [15] X.Wei and et al, "MVR: An Architecture for Computation Offloading in Mobile Edge Computing", in Edge Computing (EDGE), IEEE International Conference on. 2017.
16. [16] http://developer.android.com/guide/components/services.html ( last accessed 12 Apr 2016).
17. [17] https://msdn.microsoft.com/enus/library/dd203052.aspx.
18. [18] h.sadeghi and A. Akhavan Bitaghsir, "Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks", Signal and Data Processing, vol.14(4), p p. 19-30. 2018.

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