The Skyline Operator for Selection of Virtual Machines in Mobile Computing

Full Text (PDF, 815KB), PP.1-10

Views: 0 Downloads: 0


Rasim M. Alguliyev 1,* Ramiz M. Aliguliyev 1 Rashid G. Alakbarov 1 Oqtay R. Alakbarov 1

1. Institute of Information Technology of ANAS /Department, Baku, AZ1141, Azerbaijan

* Corresponding author.


Received: 6 Sep. 2018 / Revised: 20 Sep. 2018 / Accepted: 17 Oct. 2018 / Published: 8 Nov. 2018

Index Terms

Mobile computing clouds, mobile equipment, computing and memory resources, cloudlet, virtual machines, cloud computing, communication channel, reliability, skyline


The article provides a solution to the problem of placing mobile users’ queries (tasks or software applications) on a balanced virtual machine (VMs) developed on cloudlets placed near base stations of the Wireless Metropolitan Area Networks (WMAN) taking into account their technical capabilities. For this purpose, hierarchically structured architecture and algorithm based on cloudlets are proposed for the selection of virtual machines that provide the requirements (solution time and cost) to the solution of the user’s task. An approach to the optimal VM selection is proposed for the solution of Bi-Criteria selection out of set of VMs based on Skyline operator.

Cite This Paper

Rasim M. Alguliyev, Ramiz M. Aliguliyev, Rashid G. Alakbarov, Oqtay R. Alakbarov, " The Skyline Operator for Selection of Virtual Machines in Mobile Computing", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.11, pp. 1-10, 2018. DOI:10.5815/ijmecs.2018.11.01


[1]H. T. Dinh, C. Lee, D. Niyato, P. Wang, A survey of mobile cloud computing: Architecture, applications, and approaches. Wireless Communications and Mobile Computing. vol.13, no.18, 2013, pp.1587-1611.
[2]H. Qi, A. Gani, Research on Mobile Cloud Computing: Review, Trend and Perspectives.
[3]M. Goyal, S. Singh, Mobile Cloud Computing. International Journal of Enhanced Research in Science Technology & Engineering, vol.3, no.4, 2014, pp.517-521.
[4]T. Diaby, B. B. Rad B, “Cloud Computing: A review of the Concepts and Deployment Models”, International Journal of Information Technology and Computer Science, vol.9, no.6, 2017, pp.50-58.
[5]S. A. Elmubarak, A. Yousif, M. B. Bashir, Performance based Ranking Model for Cloud SaaS Services, International Journal of Information Technology and Computer Science, vol.9, no.1, 2017,pp.65-71.
[6]L. Liu, R. Moulic, D. Shea, “Cloud Service Portal for Mobile Device Management,” in Proceedings of IEEE 7th International Conference on e-Business Engineering, 2011, pp. 474-483.
[7]D. Kopec, M. H. Kabir, D. Reinharth, O. Rothschild, J. A. Castiglione, Human Errors in Medical Practice: Systematic Classification and Reduction with Automated Information Systems. Journal of Medical Systems, vol.27, no.4, 2013, pp.297-313.
[8]H. Gao, Y. Zhai, System Design of Cloud Computing Based on Mobile Learning, Proceedings of the 3rd International Symposium on Knowledge Acquisition and Modeling (KAM), 2010, pp.293-242.
[9]R. G. Alakbarov, F. H. Fahrad, O. R. Alakbarov, Forecasting Cloudlet Development on Mobile Computing Clouds. I.J. Information Technology and Computer Science, no.11, 2017,pp.23-34.
[10]L. Tawalbeh, N. Alassaf, W. Bakheder, A. Tawalbeh, Resilience Mobile Cloud Computing: Features, Applications and Challenges. Fifth International Conference on e-Learning, 2015, pp.280-284.
[11]R. G. Alakbarov, F. H. Pashayev, O. R. Alakbarov, Optimal Deployment Model Of Cloudlets In Mobile Cloud Computing. 2nd IEEE International Conference on Cloud Computing and Big Data Analysis (IEEE ICCCBDA 2017). Chengdu, China, April 28-30, 2017, pp.213-217.
[12]R. Alakbarov, F. Pashayev, M. Hashimov, Development of the Method of Dynamic Distribution of Users’ Data in Storage Devices in Cloud Technology. Advances in Information Sciences and Service Sciences, vol.8, no.1, 2016, pp. 16-21.
[13]O. P. Akomolafe, M. O. Abodunrin, A Hybrid Cryptographic Model for Data Storage in Mobile Cloud Computing. I. J. Computer Network and Information Security, 2017, no.6, 2017,pp. 53-60.
[14]Y. C. Shim, Effects of cloudlets on interactive applications in mobile cloud computing environments. International Journal Of Advanced Computer Technology, vol.4, no.1, 2015 pp.54-62.
[15]M. Satyanarayanan, P. Bahl, R. Caceres, N. Davies, The case for vm-based cloudlets in mobile computing, Pervasive Computing, IEEE, vol.8, no.4, 2009 pp.14-23.
[16]K. Ha, P. Pillai, W. Richter, Y. Abe, M. Satyanarayanan, “Just- in-time provisioning for cyber foraging,” in Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, 2013 pp.153-166.
[17]R. G. Alekberov, F. H. Pashayev, O. R. Alekperov, Effective Use Method of Cloudlet Resources by Mobile Users. 11th IEEE International Conference on Application of Information and Communication Technologies. Moscow, 2017 pp.401-403.
[18]M. Jia, W. Liang, Z. Xu, M. Huang, Cloudlet load balancing in wireless metropolitan area networks. The 35th Annual IEEE International Conference on Computer Communications, 10-14 April, 2016, pp 730-738.
[19]M. Jia, J. Cao, W. Liang, Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks. IEEE Transactions on Cloud Computing, vol. 5, no. 4, 2017, pp. 725-737.
[20]E. Gelenbe, R. Lent, and M. Douratsos, Choosing a local or remote cloud. Proceedings of 2nd International Symposium on Network Cloud Computing and Applications, pp. 25-30, 2012.
[21]T. Verbelen, P. Simoens, F. D. Turck, B. Dhoedt, Cloudlets: Bringing the cloud to the mobile user. Proceedings of 3rd workshop on Mobile Cloud Computing and Services, ACM, 2012, pp. 29-36.
[22]Z. Xu, W. Liang, W. Xu, M. Jia, S. Gou, Efficient Algorithms for Capacitated Cloudlet.Placements. IEEE Transactions On Parallel And Distributed Systems, vol. 27, no. 10, 2016, pp.2866-2880.
[23]P. Gupta, S. Gupta, Mobile Cloud Computing: The Future of Cloud. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. vol.1, no.3, 2012, pp.134-144.
[24]F. Liu, P. Shu, H. Jin, L. Ding, J. Yu, D. Niu, B. Li, Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. IEEE Wireless Communications. vol.20, no.3, 2013, pp.14-22.
[25]A. A. Mikryukov, R. I. Khantimirov, The task of initial allocation of resources in cloud computing environments based on the hierarchy analysis method. Applied Informatics. No.8, 2015, pp.184-185.
[26]C. Kalyvas, T. Tzouramanis, A survey of skyline query processing, arXiv:1704.01788, 2017,197 pages.
[27]X. Zhao, Y. Wu, W. Cui, X. Du, Y. Chen, Y. Wang, D. L. Lee, H. Qu, SkyLens: visual analysis of skyline on multi-dimensional data, IEEE Transactions on Visualization and Computer Graphics, vol.24, no.1, 2018, pp.246-255.
[28]A. Ouadah, A. Hadjali, F. Nader, K. Benouaret, SEFAP: an efficient approach for ranking skyline web services, Journal of Ambient Intelligence and Humanized Computing, 2018, pp.1-17.
[29]M. Bai, X. Wang, G. Li, B. Ning, Representative skyline queries with total and partial order domains using US-ELM, IEEE Access, vol.6, 2018, pp.10410-10420.
[30]C. Wang, G. Guo, X. Ye, P. S.Yu, Efficient computation of g-skyline groups, IEEE Transactions on Knowledge and Data Engineering, vol.30, no.4, 2018, pp.674-688.
[31]F. E. Bousnina, M. Chebbah, M. A. B. Tobji, A. HadjAli, B. B. Yaghlane, Skyline operator over tripadvisor reviews within the belief functions framework, Lecture Notes in Business Information Processing, vol.290, 2017, pp.186-197.
[32]A. Abidi, S. Elmi, M. A. B. Tobji, A. HadjAli, B. B. Yaghlane, Skyline queries over possibilistic RDF data, International Journal of Approximate Reasoning, vol.93, 9, 2018, pp.277-28.
[33]A. Nasridinov, J. H. Choi, Y. H. Park, A two-phase data space partitioning for efficient skyline computation, Cluster Computation, vol.20, no.4, 2017, pp.3617-3628.
[34]J. Kim, M. H. Kim, An efficient parallel processing method for skyline queries in MapReduce, The Journal of Supercomputing, vol.74, no.2, 2018 pp.886-935.
[35]Y. Park, J. K. Min, K. Shim, Efficient processing of skyline queries using MapReduce, IEEE Transactions on Knowledge and Data Engineering, vol.29, no.5, 2017, pp.1031-1044.
[36]K. Koizumi, P. Eades, K. Hiraki, M. Inaba, BJR-tree: fast skyline computation algorithm using dominance relation-based tree structure, International Journal of Data Science and Analytics, 2018, pp.1-18.
[37]D. Sarddar, R. Bose, A Mobile Cloud Computing Architecture with Easy Resource Sharing. International Journal of Current Engineering and Technology. vol.4, no.3, 2014, pp.1249-1254.
[38]S. Börzsönyi, D. Kossmann, K. Stocker, The Skyline Operator, Proceedings of the 17th International Conference on Data Engineering, 2001, pp.421-430.
[39]H. T. Kung, F. Luccio, F. P. Preparata, On finding the maxima of a set of vectors, Journal of the ACM, vol.22, no.4, 1975. pp. 469-476.