Task-Aware Load Balancing in Mobile Cloud Computing using Cloudlets
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2026.20.2.3Keywords:
big data, cloudlet, dropout rate, firefly optimization algorithm, latency, load balancing, mobile cloud computing, response time, resource schedulingAbstract
In the current era, almost every person uses a mobile device for tasks that range from basic phone calls to advanced computations. To increase the QoE of the mobile user, tasks may be offloaded to a cloudlet because of the lack of a large battery, large memory, and sufficient processing power. Cloudlets can be heavily loaded with tasks when they are in a heavily populated area, where tasks are overflowing. Meanwhile, other cloudlets are lightly loaded and their resources are often idle when they are in a moderately populated area, getting fewer tasks. When tasks are queued up for a long time in a heavily loaded cloudlet, the response time and dropout rate of tasks increase. These two parameters deteriorate further when a big task is waiting for a long time. Such big tasks waiting for a long time can be migrated to other cloudlets to utilize the idle resources available in lightly loaded cloudlets, which may provide better QoE to the user. This article uses the Firefly optimization algorithm for choosing which tasks to migrate to which cloudlet for load balancing based on task details such as total waiting task size, task waiting time, and total virtual machine size. The proposed method, called TALBMCC (Task-Aware Load Balancing in Mobile Cloud Computing), also increases the number of tasks executed by cloudlets and reduces the execution time of tasks and the power consumption of mobile devices.
Downloads
References
Satyanarayanan, M., Bahl, P., Caceres, R. & Davies, N., The Case for Vm-based Cloudlets in Mobile Computing, IEEE Pervasive Computing, 8(4), pp. 14-23, October 2009.
Cloudlets, Cloudlet-specific Match Examples, Available: https://techdocs. akamai.com/cloudlets/v2/reference/cloudlet-match (24-May-2023).
Sinky, H., Khalfi, B., Hamdaoui, B. & Rayes, A., Responsive Content-centric Delivery in Large Urban Communication Networks: A LinkNYC Use-Case, IEEE Transactions Wireless Communications, 17(3), pp. 1688-1699, 2018.
Yang, X.S., Firefly Algorithm (chapter 8). Nature-inspired Metaheuristic Algorithms, Luniver Press, Cambridge, 172, pp. 79-90, 2008.
Lai, S., Fan, X., Ye, Q., Tan, Z., Zhang, Y., He, X. & Nanda, P., FairEdge: A Fairness-Oriented Task Offloading Scheme for IoT Applications in Mobile Cloudlet Networks, IEEE Access, 8, pp. 13516-13526, 2020.
Herbert Raj, P., Ravi Kumar, P. & Jelciana, P., Load Balancing in Mobile Cloud Computing using Bin Packing?s First Fit Decreasing Method, Springer Nature, 888, pp. 97-106, 2019.
JongBeom Lim & DaeWon Lee, A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments, Electronics, 9(4), 686, 2020. doi:10.3390/electronics9040686
Ranapana, R.A.A.I.B. & Jayasena, K.P.N., Novel Approach for Load Balancing in Mobile Cloud Computing, 6th International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka, 2021, pp. 1-6, doi: 10.1109/ICITR54349.2021.9657441, 2021.
Arun, C. & Prabhu, K., An Efficient Job Sharing Strategy for Prioritized Tasks in Mobile Cloud Computing Environment using ACS-JS Algorithm, Journal of Theoretical and Applied Information Technology, 97(4), 2019.
Hussein, K.Q., A Multimedia Information Time Balance Management in Mobile Cloud Environment Supported by Case Study, International Journal of Interactive Mobile Technologies, 16(19), pp.118-132, 2022. doi: 10.3991/ijim.v16i19.33615
Lee, A., Mhatre, J., Das, R.K. & Hong, M., Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems, Computers, Materials & Continua, 74(1), pp.435-452, 2023. doi: 10.32604/cmc.2023.029340
Kanbar, A.B. & Faraj, K., Region-Aware Dynamic Task Scheduling & Resource Virtualization for Load Balancing in IoT?fog Multi-cloud Environment, Future Generation Computer Systems, 137, pp. 70-86, 2022. doi: 10.1016/j.future.2022.06.005
Fister, I., Yang, X.S., Fister, D. & Fister Jr, I., Firefly Algorithm: A Brief Review of the Expanding Literature, X.-S. Yang (ed.), Cuckoo Search and Firefly Algorithm, Studies in Computational Intelligence, 516, pp. 347-360, 2014. doi: 10.1007/978-3-319-02141-6_17
Mary, J.A. & Aloysius, A., Mobility and Execution Time Aware Task Offloading in Mobile Cloud Computing, International Journal of Interactive Mobile Technologies (iJIM), 16(15), pp. 30-45, Aug, 2022. doi:10.3991/ijim.v16i15.31589.
Mary, J.A. & Aloysius, A., Task Scheduling with Altered Grey Wolf Optimization (AGWO) in Mobile Cloud Computing using Cloudlet, Journal of Communications Software and Systems, 19(1), pp. 81-90, Jan. 2023. doi: 10.24138/jcomss-2022-0151.
