№2, 2021

DEVELOPMENT OF ALGORITHM FOR CLOUDLETS SELECTION ACCORDING TO MOBILE USERS’ REQUIREMENTS

Oktay R. Alakbarov

The recent rapid increase in the number of mobile users using mobile cloud computing (MCC) services, the availability of remote cloud servers, and the overloading of the Internet have led to significant latency in delivering processed data to users. To eliminate resource shortages, energy consumption, and latency in communication channels of mobile devices, the remote cloud servers need to be located close to users. It is offered to use cloudlet-based mobile cloud computing to reduce latency in communication channels and reduce energy consumption on mobile devices. Choosing the most suitable cloudlet to run applications quickly in cloud is still challenging. The article provides a solution to the problem of efficient use of cloudlets resources located adjacent to the base stations of wireless urban networks and close to users. Using the possible values that determine the importance of cloudlets (closeness to user, high reliability, a small number of communication channels, etc.), the conditions for determining the cloud to load the user application are studied and an algorithm is proposed (pp.108-118).

Keywords: cloud computing, mobile cloud computing, computing and memory resources, energy consumption, cloudlet, network latency, communication channels, reliability.
References
  • Akomolafe O.P., Abodunrin M.O. A Hybrid Cryptographic Model for Data Storage in Mobile Cloud Computing // J. Computer Network and Information Security, 2017, №6, pp. 53–60.
  • Shim Y. Effects of cloudlets on interactive applications in mobile cloud computing environments // International Journal of Advanced Computer Technology, 2015, vol.4, №1, pp.54-62.
  • Alekberov R.G., Pashayev F.H, Alekberov O.R. Forecasting Cloudlet Development on Mobile Computing Clouds // Information Technology and Computer Science, 2017, №11, 23–34.
  • Sahu D. Cloud Computing in Mobile Applications // International Journal of Scientific and Research Publications, 2012, vol.2, №8, pp.1–9.
  • Mukherjee A., De D., Roy R.G. A power and latency aware cloudlet selection strategy for multi-cloudlet environment // IEEE Transactions on Cloud Computing, 2016, vol.7, pp.141–154. https://doi.org/10.1109/TCC.2016.25860M.K.,61.
  • Ahmed E., Gani A.,, M.K. Khan , Buyya R., Khan S.U. Seamless application execution in mobile cloud computing: motivation, taxonomy, and open challenges // Journal of Network and Computer Application, 2015, vol.52, pp.154–172.
  • Ahmed E., Akhunzada A., Whaiduzzaman M., Gani A., Hamid S.H., Buyya R. Network-centric performance analysis of runtime application migration in mobile cloud computing // Simulation Modelling Practice and Theory, 2015 vol.50, pp.42–56.
  • Alakbarov R.G., Alakbarov, O.R. Mobile Clouds Computing: Current State, Architecture And Problems / 2nd IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT 2017), Coimbatore, India, 22–24 February, 2017, pp.1–6 . 
  • Goyal M., Singh S. Mobile Cloud Computing // International Journal of Enhanced Research in Science Technology & Engineering, 2014, vol.3, №4, 517–521.
  • Liu F. Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications // IEEE Wireless Communications, 2013, vol.20, №3, pp.14–22.
  • Qi H. Research on Mobile Cloud Computing: Review, Trend and Perspectives. https://arxiv.org/ftp/arxiv/papers/1206/1206.1118.pdf
  • Mathur R.P, Sharma M. A survey on computational offloading in mobile cloud computing / Fifth International Conference on Image Information Processing, 2019, pp.525–520. https://doi.org/10.1109/ICIIP 47207.
  • Fernando N., Loke S.W., Rahayu W. Mobile cloud computing: a survey // Future Generation Computer Systems, 2013, vol.29, pp.84–106.
  • Dinh H.T., C. Lee C., D. NiyatoD., Wang P. A survey of mobile cloud computing: architecture, applications, and approaches // Wireless Communications and Mobile Computing, 2013, vol.13, pp.1587–1611
  • Mukherjee D., De D. Low power offloading strategy for femto-cloud mobile network // Engineering Science and Technology an International Journal, 2016, vol.19, pp.260–270.
  • Tawalbeh L., Jarar Y.,Weh A., Dosari F. Large scale cloudlets deployment for efficient mobile cloud computing // Journal of Networks, 2015, vol.10, pp.70–76.
  • Quwaider M., Jararweh Y. Cloudlet-based efficient data collection in wireless body area networks // Simulation Modelling Practice and Theory, 2015,vol.50, pp.57–71.
  • Verbelen T., Simoens P.,Turck D., Dhoedt B. Adaptive deployment and configuration for mobile augmented reality in the cloudlet // Journal of Network and Computer Application, 2014. vol.41, pp.206–216.
  • Alakbarov K., Pashayev F., Hashimov M. Development of  the Method of Dynamic Distribution of Users’ Data in Storage Devices in Cloud Technology //Advances in Information Sciences and Service Sciences, 2016,vol.8, no.1, pp.16-21.
  • Akomolafe P., Abodunrin M.O. A Hybrid Cryptographic Model for Data Storage in Mobile Cloud Computing. I.J. Computer Network and Information Security,2017, no.6, pp.53-60.
  • Bohez S,, Verbelen T., Simoens P., Dhoedt B. Discrete-event simulation for efficient and stable resource allocation in collaborative mobile cloudlets // Simulation Modelling Practice and Theory 2015, vol.50, pp.109–129.
  • O’Sullivan J., Grigoras D. Integrating mobile and cloud resources management using the cloud personal assistant // Simulation Modelling Practice and Theory, 2015, vol.50, pp.20–41.
  • Beloglazov A., Abawajy J., Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing // Future Generation Computer Systems, 2012, vol.28, pp.755–768.
  • Samal P., Mishra P. Analysis of variants in round robin algorithms for load balancing in cloud computing // International of Journal Computer Science and Information Technology, 2013, vol.4, pp.416–419.
  • Zhao M., Zhou K. Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks // Algorithms, 2019, vol.12, no.2, pp.1-13.
  • Dinh H.T., Lee C., NiyatoD., P. Wang P. A survey of mobile cloud computing: Architecture, applications, and approaches // Wireless Communications and Mobile Computing, 2013, vol.13, no.18, pp.1587-1611.
  • Singh S., Chana I. QRSF: QoS-aware resource scheduling framework in cloud computing. Journal of Supercomputers, 2015, vol.71, pp.241–252.
  • Mam M., Leena G., Saxena N.S. Improved k-means clustering based distribution planning on a geographical network // International Journal of Intelligent Systems and Applications, 2017, vol.9, no.4, pp.69–75.
  • Jia M., Liang W., Xu Z., Huang M. Cloudlet Load Balancing in Wireless Metropolitan Area Networks // IEEE INFOCOM 2016 – The 35th Annual IEEE International Conference on Computer Communications, 2016, pp.1–9.
  • Nayyer M.Z., Raza I., Hussain S.A. A Survey of Cloudlet-Based Mobile Augmentation Approaches for Resource Optimization. ACM Computing Surveys, 2018, vol.51, no.5, 28 p. https://doi.org/10.1145/3241738.2018
  • Somula R.S., Sasikala R. A survey on mobile cloud computing: Mobile Computing + Cloud Computing (MCC = MC + CC) // Scalable Computing: Practice and Experience, 2018, vol.19, no.4, pp.309–337.
  • Kovachev D., Klamma R. Framework for Computation Offloading in Mobile Cloud Computing // International Journal of Artificial Intelligence and Interactive Multimedia, 2012, vol.1, №7, pp.6–15.
  • Xu Z., Liang W., Xu W. Efficient Algorithms for Capacitated Cloudlet. Placements // IEEE Transactions On Parallel And Distributed Systems, 2016, vol.27, №10, pp.2866–2880.