International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 15, No. 5, 8 Oct. 2023
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Integer Programming Models for Task Scheduling and Resource Allocation in Mobile Cloud Computing
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Author(s)
Index Terms
Mobile Cloud Computing, Task Scheduling, Resource Allocation, Integer Programming Model, Matlab 2022a
Abstract
In traditional mobile cloud computing, user tasks are uploaded and processed on a cloud server over the Internet. Due to the recent rapid increase in the number of mobile users connected to the network, due to overload of the Internet communication channels, there are significant delays in the delivery of data processed on cloud servers to the user. Furthermore, it complicates the optimal scheduling of the tasks of many users on cloud servers and the delivery of results. Scheduling is an approach used to reduce the tasks execution time by ensuring a balanced distribution of user tasks on cloud servers. The goal of scheduling is to ensure selection of appropriate resources to handle tasks quickly, taking into account user requirements. Whereas the goal of cloud service providers is to provide users with the required resources through performing effective scheduling so that both the user and the service provider can benefit. The article proposes a scheduling model to reduce processing time, network latency, and power consumption of mobile devices through optimal task placement in the cloudlet network in a mobile cloud computing environment.
Cite This Paper
Rasim M. Alguliyev, Rashid G. Alakbarov, "Integer Programming Models for Task Scheduling and Resource Allocation in Mobile Cloud Computing", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.5, pp.13-26, 2023. DOI:10.5815/ijcnis.2023.05.02
Reference
[1]R.S. Somula and S. Ra, “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, 2018.
[2]M. Ala’anzy, M. Othman, Z.M. Hanapi and M.A. Alrshah, “Locust Inspired Algorithm for Cloudlet Scheduling in Cloud Computing Environments,” Sensors, 21, 7308, pp.1-19, 2021.
[3]A. Nasr, N.A. El-Bahnasawy, G. Attiya and A. El-Sayed, “Cloudlet Scheduling Based Load Balancing on Virtual Machines in Cloud Computing Environment,” Journal of Internet Technology, vol.20, no.5, pp. 1376-1378, 2019.
[4]L. Lin, P. Li, J. Xiong and M. Lin, “Distributed and Application-aware Task Scheduling in Edge-clouds,” 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) 6-8 Dec. 2018, pp. 165-170, 2018.
[5]M. Yuyi, C. You, J. Zhang, K. Huang and K. Letaief, "A survey on mobile edge computing: The communication perspective," IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp.2322-2358, 2017.
[6]A. Nasir, Y. Zhang, A. Taherkordi and T. Skeie, "Mobile edge computing: A survey," IEEE Internet of Things Journal, vol. 5, no. 1 pp. 450-465, 2017.
[7]T. Shi, M. Yang, X. Li, Q. Lei and Y. Jiang, “An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds,” Pervasive and Mobile Computing, vol. 27, pp. 90 – 105, 2016.
[8]R.K. Alekberov, “Strategy for reducing delays and energy consumption in cloudlet-based mobile cloud computing,” International Journal of Wireless Networks and Broadband Technologies, vol.10, pp. 32-44, 2021.
[9]T. Zhao, S. Zhou, X. Guo, Y. Zhao and Z. Niu, “A cooperative scheduling scheme of local cloud and internet cloud for delay-aware mobile cloud computing,” IEEE Globecom Workshops (GC Wkshps), pp. 1–6, 2015.
[10]D.G. Roy, D.De, A. Mukherjee and R. Buyya, “Application-aware cloudlet selection for computation offloading in multi-cloudlet environment,” J Supercomputer, vol. 73, pp. 1672–1690, 2017.
[11]Z. Peng, D. Cui, J. Zuo J, Q. Li, B. Xu and W. Lin, “Random task scheduling scheme based on reinforcement learning in cloud computing,” Cluster Computing, pp.1595-1607, 2015.
[12]M. Sachula, Y. Wang, Z. Miao and K. Sun, “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, 2018.
[13]M. Quwaider and Y. Jararweh, “Cloudlet-based efficient data collection in wireless body area networks,” Simul Model Pract Theory, vol.50, pp. 57–71, 2015.
[14]F. Zhang, J. Ge, Z. Li, C. Li, C. Wong, L Kong, B. Luo and V. Chang, “A load-aware resource allocation and task scheduling for the emerging cloudlet system,” Future Generation Computer Systems. vol. 87, pp. 438-456, 2018.
[15]A. Muneera, M. Al-Ayyoub, Y.Jararweh, L.Tawalbeh and E.Benkhelifa, “Power optimization of large-scale mobile cloud system using cooperative cloudlets,” IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pp.34-38, 2016.
[16]L. Tong, Y. Li and W. Gao, “A hierarchical edge cloud architecture for mobile computing,” in: Proceedings of the 35th IEEE INFOCOM, pp. 1–9, 2016.
[17]D.K. Sajnani, A. R. Mahesar, A. Lakhan, and A. Jamali, “Latency Aware and Service Delay with Task Scheduling in Mobile Edge Computing,” Communications and Network. vol.10, no.04, 2018.
[18]A. Beloglazov, J. Abawajy and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future Gener Comput Syst, vol.28, pp.755–768, 2012.
[19]A. Mukherjee and D. De, “Low power offloading strategy for femto-cloud mobile network,” Eng Sci Technol Int J, vol. 19, pp. 260–270, 2016.
[20]Y. Liu, M. J. Lee and Y. Zheng, “Adaptive Multi-Resource Allocation for Cloudlet-Based Mobile Cloud Computing System,” IEEE Transactions on Mobile Computing, vol. 15, pp. 2398-2410, 2016.
[21]X. Sun and N. Ansari, “PRIMAL: PRofit Maximization Avatar Placement for Mobile Edge Computing,” Proceedings of 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, 22-27 May, pp. 1-6, 2016.
[22]G. Shreya, A. Mukherjee, S. Ghosh and R. Buyya, “Mobi-IoST: mobility-aware cloud-fog-edge-iot collaborative framework for time-critical applications,” IEEE Transactions on Network Science and Engineering, pp.1-15, 2019.
[23]A. Mukherjee, D. Priti, D. De and R. Buyya, "IoTF2N: “An energy-efficient architectural model for IoT using Femtoletbased fog network,” The Journal of Supercomputing, vol. 75, no. 11, pp. 7125-7146, 2019.
[24]M. Shiraz, S. Abolfazli, Z. Sanaei and A. Gani, “A Study on Virtual Machine Deployment for Application Outsourcing in Mobile Cloud Computing,” The Journal of Supercomputing, vol. 63, pp. 946-964, 2013.
[25]N. Kumar and R. Kumar, “Scheduling of Tasks (Cloudlets) in Heterogeneous Processing Cloud Environment,” International Journal on Emerging Technologies, vol. 11, no.3, pp. 417-421, 2020.
[26]C. Gogos, C. Valouxis, P.Alefragis, G.Goulas, N.Voros and E.Housos, “Scheduling independent tasks on heterogeneous processors using heuristics and column pricing,” Future Gener Comp Syst, vol. 60, pp. 48-66, 2016.
[27]L. Liu, M. Zhang, R. Buyya and Q. Fan, “Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing,” Concur Comput: Pract Exper. vol. 12, pp.3942-3954, 2017.
[28]P. Zhang and M. Zhou, “Dynamic cloud task scheduling based on a two-stage strategy,” IEEE Trans Autom Scie Eng, pp.772-783, 2018.
[29]R.G. Alekberov and O.R. Alekperov, “Procedure of effective use of cloudlets in wireless metropolitan area network environment,” International Journal of Computer Networks & Communications, vol. 11, no. 1, pp.93–107, 2019.
[30]Y. Shen, Z. Bao, X. Qin and J. Shen, “Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee,” World Wide Web. pp. 155-173, 2016.
[31]E. Ahmed, A. Akhunzada, M. Whaiduzzaman, A. Gani, S.H. Ab Hamid and R. Buyya, “Network-centric performance analysis of runtime application migration in mobile cloud computing,” Simul Model Pract Theory, vol. 50, pp. 42–56, 2015.
[32]P. Azad and N.J. Navimipour, “An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm,” Int J Cloud Appl Comput. pp.20-40, 2017.
[33]G.H. Bindu, K.Ramani, C.S. Bindu, “Energy aware multi objective genetic algorithm for task scheduling in cloud computing,” Int J Int Protocol Tech. pp. 242-249, 2018.