The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability
Abstract
Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.
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“If an edge node experiences a failure or disruption, the cloud server can seamlessly take over the processing tasks, which ensures continuous operation and minimizes downtime [27].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [27]
Citations
Cites
A survey of mobile cloud computing: architecture, applications, and approaches
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013) · Wireless Communications and Mobile Computing · doi:10.1002/wcm.1203 · Cited by 2,391 · cited 1×
“One recent popular research field for providing high-computational capability service is mobile cloud computing (MCC) [3], which can offload the computation tasks of MDs to the more powerful cloud center(CC) and centralized process the tasks.”
BackgroundEnergy-Efficient Resource Allocation for Mobile-Edge Computation Offloading
You, C., Huang, K., Chae, H., & Kim, B. (2016) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2633522 · Cited by 1,516
User Association for Load Balancing in Heterogeneous Cellular Networks
Ye, Q., Rong, B., Chen, Y., Al‐Shalash, M., Caramanis, C., & Andrews, J. G. (2013) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2013.040413.120676 · Cited by 1,223
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Sardellitti, S., Scutari, G., & Barbarossa, S. (2015) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2015.2448520 · Cited by 889
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., Li, P., Maharjan, S., & Zhang, K. (2016) · IEEE Access · doi:10.1109/access.2016.2597169 · Cited by 858
Collaborative Cloud and Edge Computing for Latency Minimization
Ren, J., Yu, G., He, Y., & Li, G. Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2904244 · Cited by 685
Beck, A., Ben‐Tal, A., & Tetruashvili, L. (2009) · Journal of Global Optimization · doi:10.1007/s10898-009-9456-5 · Cited by 600
Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Wang, J., Zhao, L., Liu, J., & Kato, N. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2902661 · Cited by 460
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading
Ren, J., Yu, G., Cai, Y., & He, Y. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2845360 · Cited by 452
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges
Abolfazli, S., Sanaei, Z., Ahmed, E., Gani, A., & Buyya, R. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.070813.00285 · Cited by 441
Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture
Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., & Heinzelman, W. (2012) · 2012 IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2012.6249269 · Cited by 420
Wang, C., Yu, F. R., Liang, C., Chen, Q., & Tang, L. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2672701 · Cited by 369
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Xu, J., Chen, L., & Ren, S. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2725277 · Cited by 368
Joint Computation Offloading and User Association in Multi-Task Mobile Edge Computing
Dai, Y., Xu, D., Maharjan, S., & Zhang, Y. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2876804 · Cited by 367
Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications
Yang, L., Cao, J., Cheng, H., & Ji, Y. (2014) · IEEE Transactions on Computers · doi:10.1109/tc.2014.2366735 · Cited by 243
Hermes: Latency Optimal Task Assignment for Resource-constrained Mobile Computing
Kao, Y.-H., Krishnamachari, B., Ra, M.-R., & Bai, F. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2679712 · Cited by 241
Zhang, J., Xia, W., Yan, F., & Shen, L. (2018) · IEEE Access · doi:10.1109/access.2018.2819690 · Cited by 239
A game-theoretic approach to computation offloading in mobile cloud computing
Cardellini, V., Personé, V. D. N., Valerio, V. D., Facchinei, F., Grassi, V., Presti, F. L., & Piccialli, V. (2015) · Mathematical Programming · doi:10.1007/s10107-015-0881-6 · Cited by 203
Fast Converging Algorithm for Weighted Sum Rate Maximization in Multicell MISO Downlink
Tran, L., Hanif, M. F., Tölli, A., & Juntti, M. J. (2012) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2223211 · Cited by 176
Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing
Yu, Y., Zhang, J., & Letaief, K. B. (2016) · Globecom · doi:10.1109/glocom.2016.7841937 · Cited by 171
MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing
Rahimi, M. R., Venkatasubramanian, N., & Vasilakos, A. V. (2013) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2013.100 · Cited by 147
Cheng, K., Teng, Y., Sun, W., Liu, A., & Wang, X. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422877 · Cited by 143
A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing
Yang, L., Cao, J., Tang, S., Li, T., & Chan, A. (2012) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2012.97 · Cited by 139
Energy-Efficient Device-to-Device Communications for Green Smart Cities
Kai, C., Li, H., Xu, L., Li, Y., & Jiang, T. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2789304 · Cited by 103
Energy Efficiency and Spectral Efficiency Tradeoff in Interference-Limited Wireless Networks
Li, Y., Sheng, M., Yang, C., & Wang, X. (2013) · IEEE Communications Letters · doi:10.1109/lcomm.2013.082613.131286 · Cited by 91
Yang, X., Yu, X., Huang, H., & Zhu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2936435 · Cited by 83
Jiao, L., Fu, X., Friedman, R., Secci, S., Smoreda, Z., Tschofenig, H., Cunningham, P., & Cunningham, M. (2013) · GRO · Cited by 59
Huang, W., Huang, Y., Zeng, Y., & Yang, L. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2860963 · Cited by 51
Cloud-Edge Coordinated Processing: Low-Latency Multicasting Transmission
He, S., Ren, J., Wang, J., Huang, Y., Zhang, Y., Zhuang, W., & Shen, S. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2906791 · Cited by 46
Joint Offloading and Resource Allocation Optimization for Mobile Edge Computing
Zhang, J., Xia, W., Zhang, Y., Zou, Q., Huang, B., Yan, F., & Shen, L. (2017) · Globecom · doi:10.1109/glocom.2017.8254634 · Cited by 40
Asheralieva, A., & Niyato, D. T. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2994366 · Cited by 37
Huang, W., Huang, Y., Zhao, R., He, S., & Yang, L. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2861903 · Cited by 36
Macro-Cell Assisted Task Offloading in MEC-Based Heterogeneous Networks With Wireless Backhaul
Haber, E. E., Nguyen, T. M., Assi, C. M., & Ajib, W. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2939685 · Cited by 32
Cloud and Edge Multicast Beamforming for Cache-Enabled Ultra-Dense Networks
Huang, W., Huang, Y., He, S., & Yang, L. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2968466 · Cited by 28
Two-Level Transmission Scheme for Cache-Enabled Fog Radio Access Networks
He, S., Qi, C., Huang, Y., Hou, Q., & Nallanathan, A. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2018.2869562 · Cited by 24
Cache-Enabled Coordinated Mobile Edge Network: Opportunities and Challenges
He, S., Huang, W., Wang, J., Ren, J., Huang, Y., & Zhang, Y. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900317 · Cited by 24
Kobayashi, R., & Adachi, K. (2019) · IEEE Access · doi:10.1109/access.2019.2944184 · Cited by 18
Reinforcement Learning-Based Computing and Transmission Scheduling for LTE-U-Enabled IoT
He, H., Shan, H., Huang, A., Ye, Q. J., & Zhuang, W. (2018) · Globecom · doi:10.1109/glocom.2018.8647178 · Cited by 16
Joint Communication and Computation Resource Optimization for NOMA-Assisted Mobile Edge Computing
Mao, S., Leng, S., & Zhang, Y. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761996 · Cited by 16
Computation offloading balance in small cell networks with mobile edge computing
Chen, L., Li, X., Ji, H., & Leung, V. C. M. (2018) · Wireless Networks · doi:10.1007/s11276-018-1735-y · Cited by 11
Flagged first, then by citations
Research works printed but not linked: 3
- 2014, Mobile-Edge Computing-Introductory Technical White Paper
- grant, 2014, CVX Matlab Software for Disciplined Convex Programming Version 2 1
- ikuno, 2010, 3GPP TR 36 814 v9 0 0-evolved universal terrestrial radio access (e-UTRA) further advancements for e-utra physical layer aspecs
Cited by
Sorted by citations
A Survey on Metaverse: Fundamentals, Security, and Privacy
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,655
Liu, J. B., Xiang, J., Jin, Y., Liu, R., Yan, J., & Wang, L. (2021) · Remote Sensing · doi:10.3390/rs13214387 · Cited by 212
Akhlaqi, M. Y., & Hanapi, Z. M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103568 · Cited by 172
Reverse Auction-Based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing
Zhou, H., Wu, T., Chen, X., He, S., Guo, D., & Wu, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3189050 · Cited by 165
A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing
Wang, B., Wang, C., Huang, W., Song, Y., & Qin, X. (2020) · IEEE Access · doi:10.1109/access.2020.3029649 · Cited by 157
Metadata
- Authors
- Kai, C., Zhou, H., Yi, Y., & Huang, W.
- Affiliations
- Hefei University of Technology (CN)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2021-06
- Volume
- 7
- Issue
- 2
- Pages
- 624-634
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W3080122404
- Cited by
- 249
- References
- 40
- Citations by year
- 2026: 30 · 2025: 50 · 2024: 52 · 2023: 63 · 2022: 38 · 2021: 14
- FWCI
- 13.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- Funders
- National Natural Science Foundation of China (61971176, 61901156)
- Anhui Provincial Natural Science Foundation (2008085QF281)
- Fundamental Research Funds for the Central Universities of China (PA2020GDKC0008, JZ2019HGBZ0144)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- mobile edge computing, task offloading, cloud-edge-end collaboration, latency reduction, power allocation, successive convex approximation, non-convex optimization, mobile devices
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