Research on Resource Allocation and Management of Mobile Edge Computing Network
DOI:
https://doi.org/10.31449/inf.v44i2.3166Abstract
The popularity of mobile Internet makes the application of mobile terminals need more computing resources, and cloud computing enables mobile terminals to handle application tasks that need high computing resources under the premise of maintaining small specifications. However, it is difficult to obtain high-quality low latency services as the mobile Internet edge is far away from the cloud computing center; hence mobile edge computing (MEC) is proposed. This study introduced computing resource allocation methods based on power iteration and system utility, applied them to the mobile edge computing network, and carried out simulation experiments in MATLAB software. The experimental results showed that the network throughput and system utility under the two resource allocation methods increased and the average transfer rate decreased with the increase of users in the mobile edge network; under the same number of access users, the edge network based on the system utility allocation method had higher throughput, average transfer rate and system utility.References
Al-Shuwaili A, Simeone O (2016). Energy-Efficient Resource Allocation for Mobile Edge Computing- Based Augmented Reality Applications. IEEE Wireless Communication Letters, PP(99). https://doi.org/10.1109/LWC.2017.2696539
Ahmed E, Rehmani MH (2016). Mobile Edge Computing: Opportunities, solutions, and challenges. Future Generation Computer Systems, 70. https://doi.org/10.1016/j.future.2016.09.015
Paymard P, Mokari N (2019). Resource allocation in PD-NOMA–based mobile edge computing system: Multiuser and multitask priority. Transactions on Emerging Telecommunications Technologies, (1), pp. e3631. https://doi.org/10.1002/ett.3631
Liu ZK, Yang XQ, Shen JX (2019). Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture. Design Automation for Embedded Systems, (4). https://doi.org/10.1007/s10617-019-09222-5
Zhang F, Liu G, Zhao B, Fu X, Yahyapour R (2018). Reducing the network overhead of user mobilityinduced virtual machine migration in mobile edge computing. Software Practice and Experience, (3). https://doi.org/10.1002/spe.2642
Hao Y, Chen M, Hu L, Hossain MS, Ghoneim A (2018). Energy Efficient Task Caching and Offloading for Mobile Edge Computing. IEEE Access, 6(99), pp. 11365-11373. https://doi.org/10.1109/ACCESS.2018.2805798
Pham QV, Le LB, Chung SH (2019). Mobile Edge Computing with Wireless Backhaul: Joint Task Offloading and Resource Allocation. IEEE Access, PP(99), pp. 1-1. https://doi.org/10.1109/access.2018.2883692
Ma LL, Yi SH, Carter N, Li Q (2018). Efficient Live Migration of Edge Services Leveraging Container Layered Storage. IEEE Transactions on Mobile Computing, PP(99), pp. 1-1. https://doi.org/10.1109/TMC.2018.2871842
Farris I, Taleb T, Flinck H (2018). Providing ultrashort latency to user‐centric 5G applications at the mobile network edge. Transactions on Emerging Telecommunications Technologies, 29. https://doi.org/10.1002/ett.3169
Shahzadi S, Iqbal M, Dagiuklas T, Qayyum ZU (2017). Multi-Access Edge Computing: Open issues, Challenges and Future Perspective. Journal of Cloud Computing Advances Systems & Applications, 6(1), pp. 30. https://doi.org/10.1186/s13677-017-0097-9
An N, Yoon S, Ha T, Kim Y, Lim H (2018). Seamless Virtualized Controller Migration for Drone Applications. IEEE Internet Computing, PP(99), pp. 1-1. https://doi.org/10.1109/MIC.2018.2884670
Zeng DZ, Gu L, Pan SL, Cai JJ, Guo S (2019). Resource Management at the Network Edge: A Deep Reinforcement Learning Approach. IEEE Network, 33(3), pp. 26-33. https://doi.org/10.1109/MNET.2019.1800386
Wang Z, Zhao ZW, Min GY (2018). User mobility aware task assignment for Mobile Edge Computing. Future Generation Computer Systems, 85. https://doi.org/10.1016/j.future.2018.02.014
Fang WW, Ding S, Li YY (2019). OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems. Wireless Networks, 25(5). https://doi.org/10.1007/s11276- 019-02000-y
Yang X, Chen ZY, Li KK (2018). Communication- Constrained Mobile Edge Computing Systems for Wireless Virtual Reality: Scheduling and Tradeoff. IEEE Access, 6, pp. 16665-16677. https://doi.org/10.1109/ACCESS.2018.2817288
Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
