International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.14, No.3, Jun. 2022

Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets

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Rashid G. Alakberov

Index Terms

Mobile cloud computing;edge computing;cloudlet;power consumption;communication channel;file types;network delays;communication channel bandwidth;cluster


The rapid increase in the number of mobile phones and IoT devices connected to the network reduces the bandwidth of the Internet communication channel, and as a result, delays occur in the delivery of data processed in remote clouds. Edge computing systems (cloudlet, fog computing, etc.) are used to eliminate resource shortages, energy consumption, and communication channel delays in mobile devices. Edge computing systems place processing devices (computers) close to users. Cloudlet-based mobile cloud computing is widely used to reduce delays in communication channels and energy consumption in mobile devices. Selection of the most suitable cloudlet allowing users to run applications fast in cloud is still a considerable problem. This paper proposes a strategy for the selection of high-performance cloudlets providing fast solutions, considering the complexity of application (file type). It offers a method for cloudlet selection out of large number of cloudlets with different technical capabilities providing faster processing of user application. The timing of user applications in cloudlets with different technical capabilities (operating frequency, number of cores, volume of RAM, etc.) also varies. The proposed method provides faster solution for the user application. User applications are grouped by type of application, and a set of cloudlets are clustered by the number of groups. Clustering is performed first by the parameters corresponding to the operating frequency of the cloudlets, then by the number of cores and the volume of RAM. The proposed method reduces energy consumption of mobile devices by providing faster processing of applications. Thus, the proposed strategy provides an energy consumption reduction on mobile devices, faster processing of results and decrease of network delays.

Cite This Paper

Rashid G. Alakberov, "Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets", International Journal of Computer Network and Information Security(IJCNIS), Vol.14, No.3, pp.75-87, 2022. DOI: 10.5815/ijcnis.2022.03.06


[1]Global No.1 Business Data Platform: /statistics/738977/worldwide-monthly-data-traffic-per-smartphone.

[2]M.Yuyi, C. You, J. Zhang, K. Huang and K. Letaief. (2017). "A survey on mobile edge computing: The communication perspective." IEEE Communications Surveys & Tutorials vol. 19, no. 4, pp.2322-2358.

[3]A. Nasir, Y. Zhang, A. Taherkordi and T. Skeie. (2017).  "Mobile edge computing: A survey." IEEE Internet of Things Journal vol. 5, no. 1 pp.: 450-465.

[4]A. Mukherjee, D, Soumya, K. Ghosh and R. Buyya. (2021). “Introduction to Mobile Edge Computing”. November 2021.DOI:10.1007/978-3-030-69893-5_1.In book: Mobile Edge Computing.pp.(3-19).

[5]A. Muneera, M. Al-Ayyoub, Y. Jararweh, L. Tawalbeh and E. Benkhelifa. (2016)."Power optimization of large-scale mobile cloud system using cooperative cloudlets." In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pp.34-38.

[6]M. Sachula, Y. Wang, Z. Miao and K.Sun. (2018)."Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment." Peer-to-Peer Networking and Applications, vol. 11, no. 3, pp. 462-472.

[7]R.P. Mathur and M. Sharma. (2019). “A survey on computational offloading in mobile cloud computing”. 2019 Fifth International Conference on Image Information Processing, 8985893, pp.525-520.2019. 47207.

[8]R.K Alekberov. (2021). “Strategy for reducing delays and energy consumption in cloudlet- based mobile cloud computing,” International Journal of Wireless Networks and Broadband Technologies, vol. 10, no.1, pp. 32-44.

[9]R.S. Somula, S. Ra. (2018). “A survey on mobile cloud computing: Mobile Computing + Cloud Computing (MCC = MC + CC).” Scalable Computing: Practice and Experience, vol. 19, no. 4, pp. 309–337. 

[10]A. Mukherjee and D. De. (2016). “Low power offloading strategy for femto-cloud mobile network”. Eng Sci Technol Int J, vol.19, pp.260–270.

[11]R. Hassan, N. Yazdani and R. Shojaee. (2017)."Modeling and performance analysis of cloudlet in Mobile Cloud Computing." Performance Evaluation, vol. 107, pp. 34-53.

[12]S. Xiang and N. Ansari. (2020)."Green cloudlet network: A sustainable platform for mobile cloud computing." IEEE Transactions on Cloud Computing, vol. 8, Issue: 1, pp.180-192.

[13]A. Boukerche, S. Guan, R.E.De. Grand. (2020). “Sustainable Offloading in Mobile Cloud Computing: Algorithmic Design and Implementation.” ACM Computing Surveys, vol.  52, Issue 1, no. 11, pp. 1–37,

[14]E. Ahmed, A. Akhunzada, M. Whaiduzzaman, A. Gani, S.H. Ab Hamid and R. Buyya (2015). Network-centric performance analysis of runtime application migration in mobile cloud computing. Simul Model Pract Theory, vol.50, pp.42–56.

[15]G. Shreya, A. Mukherjee, S. Ghosh and R. Buyya. (2019). "Mobi-IoST: mobility-aware cloud-fog-edge-iot collaborative framework for time-critical applications." IEEE Transactions on Network Science and Engineering: DOI 10.1109/TNSE.2019.2941754, IEEE, pp.1-15.

[16]A. Mukherjee, D. Priti, D. De and R. Buyya. (2019). "IoTF2N: “An energy-efficient architectural model for IoT using Femtoletbased fog network." The Journal of Supercomputing, vol. 75, no. 11, pp.7125-7146

[17]W. Shi, S. Dustdar. (2016). “The Promise of Edge Computing”. In: The Promise of Edge Computer. Computing, vol. 49, no. 5, pp. 78–81.

[18]Rida Qayyum, Hina Ejaz, " Data Security in Mobile Cloud Computing: A State of the Art Review", International Journal of Modern Education and Computer Science(IJMECS), Vol.12, No.2, pp. 30-35, 2020.DOI: 10.5815/ijmecs.2020.02.04

[19]X. Zhu, L. T. Yang, H. Chen, J. Wang, S. Yin, and X. Liu, “Real Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds,” IEEE Trans. Cloud Computing, vol. 2, no. 2, pp. 168- 180.

[20]G.Yan, S. Wang, A. Zhou, J. Xu, J. Yuan and C. Hsu. (2020). "User allocation‐aware edge cloud placement   in mobile edge computing." Software: Practice and Experience, vol. 50, no. 5, pp. 489-502.

[21]M. Quwaider and Y. Jararweh. (2015). “Cloudlet-based efficient data collection in wireless body area    networks”. Simul Model Pract Theory, vol.50, pp.57–71.

[22]T. Verbelen, P. Simoens, F.D. Turck and B. Dhoedt. (2014). “Adaptive deployment and configuration for mobile augmented reality in the cloudlet”. J Netw Comput Appl, vol.41, pp. 206–216.

[23]Adil Bashir, Sahil Sholla, " Resource Efficient Security Mechanism for Cloud of Things", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.11, No.4, pp. 41-45, 2021.DOI: 10.5815/ijwmt.2021.04.05

[24]Kapan Oralgazyolu Shakerkhan, Ermek Tolegenovich Abilmazhinov, " Development of a Method for Choosing Cloud Computing on the Platform of Paas for Servicing the State Agencies", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.9, pp. 14-25, 2019.DOI: 10.5815/ijmecs.2019.09.02 

[25]V. Kovtun, I. Izonin, M. Gregus. (2021). “Mathematical models of the information interaction process in 5G-IoT ecosystem: Different functional scenarios.” ICT Express, pp. 1-6, /

[26]H. Mora, M. Gimeno, M. T. Signes-Pont and B. Volckaert. (2019). “Multilayer Architecture Model for Mobile Cloud Computing Paradigm”. Complexity.Volume, Article ID 3951495, 13 pages.

[27]A. Mukherjee, D. De and R. G. Roy. (2016). “A power and latency aware cloudlet selection strategy for multi-cloudlet environment”. IEEE Transactions on Cloud Computing, vol.7, pp.141-154.

[28]M. Zhao and K. Zhou. (2019). “Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks”. Algorithms, vol.12, no.2, pp.1-13.

[29]S. Banerjee, M. Adhikari, S. Kar and U. Biswas, (2015). “Development and Analysis of A New Cloudlet   Allocation Strategy for QoS Improvement in Cloud,” Arabian Journal for Science and Engineering, vol. 40, no. 5, pp. 1409-1425.

[30]R.G.Alekberov and O.R. Alekperov. (2019).  “Procedure of effective use of cloudlets in wireless metropolitan area network environment". International Journal of Computer Networks & Communications (IJCNC), vol.11, no.1, pp.93–107.

[31]Manju Mam, Leena G, N S Saxena, "Improved K-means Clustering based Distribution Planning on a Geographical Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.4, pp.69-75, 2017. DOI: 10.5815/ijisa.2017.04.08

[32]R.K. Alekberov, (2021). “Challenges of Mobile Devices’ Resources and in Communication Channels and their Solutions” International Journal of Computer Network and Information Security (IJCNIS). vol.13, no.1, pp. 39-46.

[33]M. Jia, W. Liang, Z. Xu and M. Huang (2016). “Cloudlet Load Balancing in Wireless Metropolitan Area Networks”. IEEE INFOCOM 2016 – The 35th Annual IEEE International Conference on Computer Communications, pp.1-9.

[34]G.T. Hicham and E.A. Chaker (2016). “Cloud Computing CPU Allocation and Scheduling Algorithms Using CloudSim Simulator”. International Journal of Electrical and Computer Engineering, vol.6, no.4, pp.1866-1879.

[35]K. Gai, M. Qiu, H. Zhao, L.Tao and Z. Zong. (2016). “Dynamic Energy-aware Cloudlet-based Mobile Cloud Computing Model for Green Computing”. Journal of Network and Computer Applications, vol. 59, pp. 46-54.

[36]D. G. Roy, D.De, A. Mukherjee and R. Buyya. (2017). “Application-aware cloudlet selection for computation offloading in multi-cloudlet environment”.J Supercomput 73: pp. 1672–1690.DOI 10.1007/s11227-016-1872-y.

[37]M. Jia, J. Cao, W. Liang. (2017). “Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks.” IEEE Transactions on Cloud Computing, vol. 5, no. 4, pp. 725-737.