The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A computation offloading method over big data for IoT-enabled cloud-edge computing
Abstract
Abstract The Internet of mobile things is a burgeoning technique that generates, stores and processes big real-time data to render rich services for mobile users. In order to mitigate conflicts between the resource limitation of mobile devices and users’ demands of decreasing processing latency as well as prolonging battery life, it spurs a popular wave of offloading mobile applications for execution to centralized and decentralized data centers, such as cloud and edge servers. Due to the complexity and difference of mobile big data, arbitrarily offloading the mobile applications poses a remarkable challenge to optimizing the execution time and the energy consumption for mobile devices, despite the improved performance of Internet of Things (IoT) in cloud-edge computing. To address this challenge, we propose a c omputation o ffloading m ethod, named COM, for IoT-enabled cloud-edge computing. Specifically, a system model is investigated, including the execution time and energy consumption for mobile devices. Then dynamic schedules of data/control-constrained computing tasks are confirmed. In addition, NSGA-III (non-dominated sorting genetic algorithm III) is employed to address the multi-objective optimization problem of task offloading in cloud-edge computing. Finally, systematic experiments and comprehensive simulations are conducted to corroborate the efficiency of our proposed method.
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Citations
Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
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A survey on deep learning for big data
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Middleware for Internet of Things: A Survey
Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2498900 · Cited by 1,024
Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling
Thinh, T. Q., Tang, J., La, Q. D., & Quek, T. Q. S. (2017) · IEEE Transactions on Communications · doi:10.1109/tcomm.2017.2699660 · Cited by 866
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning
Jeong, S., Simeone, O., & Kang, J. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2706308 · Cited by 812
Wireless energy harvesting for the Internet of Things
Kamalinejad, P., Mahapatra, C., Sheng, Z., Mirabbasi, S., M. Leung, V. C., & Guan, Y. L. (2015) · IEEE Communications Magazine · doi:10.1109/mcom.2015.7120024 · Cited by 664
IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges
Cai, H., Xu, B., Jiang, L., & Vasilakos, A. V. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2619369 · Cited by 588
INFOCOM, 2011 Proceedings IEEE
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To offload or not to offload? The bandwidth and energy costs of mobile cloud computing
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Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
“There are also some works [121], [204]– [208] utilizing the non-dominated sorting genetic algorithm (NSGA) to solve the formulated multi-objective optimization problem.”
BackgroundMethodInfluentialEdge-computing-driven Internet of Things: A Survey
Kong, L., Tan, J., Huang, J., Chen, G., Wang, S., Jin, X., Zeng, P., Khan, M., & Das, S. K. (2023) · ACM Computing Surveys · doi:10.1145/3555308 · Cited by 396
Bi, J., Yuan, H., Duanmu, S., Zhou, M., & Abusorrah, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3024223 · Cited by 365
Blockchain for Edge of Things: Applications, Opportunities, and Challenges
Gadekallu, T. R., Pham, Q.-V., Nguyen, D. C., Maddikunta, P. K. R., Deepa, N., Prabadevi, B., Pathirana, P. N., Zhao, J., & Hwang, W.-J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119639 · Cited by 217
Goudarzi, M., Palaniswami, M., & Buyya, R. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3123165 · Cited by 155
Metadata
- Authors
- Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., & Qi, L.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 95
- Pages
- 522-533
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2911353037
- Cited by
- 336
- References
- 40
- FWCI
- 31.12
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., & Qi, L. (2019). A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.12.055
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