Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Jiaqi Ge
 -- 1185 2023-08-10 07:43:55 |
2 format correct
Catherine Yang
 Meta information modification 1185 2023-08-10 08:14:24 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Ge, J.; Xu, G.; Zhang, Y.; Lu, J.; Chen, H.; Meng, X. D2D-Assisted Caching-Enhanced MEC System. Encyclopedia. Available online: https://encyclopedia.pub/entry/47875 (accessed on 08 July 2024).
Ge J, Xu G, Zhang Y, Lu J, Chen H, Meng X. D2D-Assisted Caching-Enhanced MEC System. Encyclopedia. Available at: https://encyclopedia.pub/entry/47875. Accessed July 08, 2024.
Ge, Jiaqi, Gaochao Xu, Yang Zhang, Jianchao Lu, Haihua Chen, Xiangyu Meng. "D2D-Assisted Caching-Enhanced MEC System" Encyclopedia, https://encyclopedia.pub/entry/47875 (accessed July 08, 2024).
Ge, J., Xu, G., Zhang, Y., Lu, J., Chen, H., & Meng, X. (2023, August 10). D2D-Assisted Caching-Enhanced MEC System. In Encyclopedia. https://encyclopedia.pub/entry/47875
Ge, Jiaqi, et al. "D2D-Assisted Caching-Enhanced MEC System." Encyclopedia. Web. 10 August, 2023.
D2D-Assisted Caching-Enhanced MEC System
Edit
This entry is adapted from the peer-reviewed paper 10.3390/electronics12153249

In the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. However, establishing a direct connection to the MEC server is not always feasible for certain devices. The system leverages idle helper devices to execute and offload tasks to the MEC server, thereby enhancing resource utilization and reducing offload time. To further minimize offloading time for latency-sensitive tasks, this research incorporates edge caching.

D2D-assisted caching-enhanced MEC system joint optimization

1. Introduction

The proliferation of mobile terminal equipment and the increasing complexity of the Internet-of-Things (IoT)-device applications have highlighted the limitations of mobile cloud computing [1][2], such as long transmission delays and heavy central server loads. In response, Mobile Edge Computing (MEC) [3][4] has emerged as a promising solution, providing abundant computing, communication and storage resources for mobile devices in close proximity. This technology facilitates task offloading, reduces latency and enables agile mobile services [5].
With the advent of 5th generation (5G) [6] wireless systems and the growing complexity of application programs, there has been a surge in large-scale intelligent devices connecting to edge servers [6]. However, some devices face challenges in establishing a direct connection to the MEC server due to limited network coverage, intermittent connectivity or hardware constraints. Additionally, the execution of latency-sensitive tasks requires faster processing times. Therefore, it is crucial to find efficient ways for these devices to execute tasks with minimal overhead and high speed to drive further advancements in mobile edge computing.
Device-to-Device (D2D) communication has been proposed as a solution to reduce delay and energy consumption through cooperative behavior among mobile devices [7][8][9]. Integrating D2D with mobile edge computing brings together the advantages of localized computing, reduced latency, improved efficiency and enhanced reliability, offering a promising solution for various mobile applications and services.

2. Joint Optimization of Computation, Communication and Caching in D2D-Assisted Caching-Enhanced MEC System

As for the MEC system, the combination of D2D and MEC enhances the computing ability of the system and shortens transmission distance where the nearby devices provide close support for simple tasks, while computing-intensive tasks can further obtain sufficient computing and storage resources at the cost of long-distance transmission in the MEC server. There are many types of research on the D2D-assisted MEC system. Ouamri et al. [10] primarily focus on the application of Device-to-Device (D2D) communication in the context of Unmanned Aerial Vehicles (UAVs), aiming to improve energy efficiency. Lingjun Pu et al. [11] put forward a new mobile task-unloading framework based on D2D cooperation, which realizes the dynamic sharing of computing and communication resources through the control assistance of network operators. Dan Wu et al. [12] put forward a dynamic distributed-resource-sharing scheme, which is applied to the unified framework of general D2D communication by jointly optimizing mode selection, resource allocation and power allocation. Yinghui He et al. [13] integrate D2D communications with MEC to improve the computation capacity of the cellular networks, aiming to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. Y Dai et al. [14] develop the interlay mode as a unique D2D model to maximize the system sum rate in the non-orthogonal multiple access (NOMA) cellular networks. However, tasks are offloaded to the MEC server or devices nearby as above. Almost none of them consider the circumstance that mobile devices cannot connect to the MEC server directly in the case of no Internet access or poor signal.
Although the D2D-assisted MEC system solves the problems of limited computing resources and power of mobile devices by reasonable offloading for large-scale and complex tasks, shortening the task-offloading process and reducing the offloading-task data are effective ways to reduce energy consumption further. Therefore, researchers have begun to combine edge caching [15][16] with the MEC system. Reference [17] proposes a D2D-cache strategy using multi-agent reinforcement learning. The D2D-cache problem is formalized as a bandit problem with multiple agents and weapons and Q-learning is used to learn how to coordinate cache decisions. Reference [18] investigates a joint pushing and caching policy in a general mobile-edge-computing (MEC) network with multi-user and multi-cast data based on hierarchical-reinforcement learning. Reference [19] investigates the collaborative-caching problem in the edge-computing environment to minimize the system cost and proposes an online algorithm called CEDC-O to solve this problem. Reference [20] studies edge caching in fog-computing networks, where a capacity-aware edge-caching framework is proposed by considering both the limited fog-cache capacity and the connectivity capacity of base stations. Reference [21] studies the unique problem of caching fairness in edge-computing environments. Reference [22] formulates the Edge Data Caching (EDC) problem as a constrained-optimization problem to minimize the caching-data cost and maximize the reduction in service latency.
Regarding edge caching, most researchers pay more attention to content caching [23][24]. Yu, G et al. [25] propose a content-caching strategy based on mobility prediction and joint-user prefetching. J. Guo et al. [26] propose a novel context-aware object-detection method based on edge-cloud cooperation. Y. Dai et al. [27] integrate DRL and permit blockchain into vehicular networks for intelligent and secure content caching. Wu W et al. [28] propose a content-based D2D cooperative edge-caching strategy. The content can be cached in the user equipment or the surrounding small base stations according to popularity. However, except for content, the task’s size, popularity and complexity are also important factors that deserve consideration.
In addition, the complexity of tasks further accelerates the energy consumption of mobile devices, causing battery consumption to be a critical factor in restricting the development of mobile devices. Meanwhile, executing tasks with low power will prolong the execution time, which cannot meet the quality of service requirements of users. To this end, it is necessary to manage resources reasonably to reduce the energy consumption of mobile devices. Reference [29] focuses on resource allocation in a downlink 5G tri-sectorial cell for non-orthogonal multiple access (NOMA) systems. Reference [30] proposes a proportional fair-based scheduler algorithm that incorporates Signal Interference Noise Ratio (SINR) compensation to address the challenge of resource allocation in cellular networks. Reference [31] studies resource allocation for a multi-user mobile-edge-computation-offloading system under infinite or finite cloud-computation capacity. Reference [32] investigates the latency-minimization problem in a multi-user time-division multiple access mobile-edge-computation-offloading system with joint communication- and computation-resource allocation. Reference [33] studies the problem of joint task offloading and resource allocation to maximize the users’ task-offloading gains. Reference [34] proposes an integrated framework for computation offloading and interference management in wireless cellular networks with MEC, which formulates the computation-offloading decision, physical-resource-block (PRB) allocation. Reference [35] designs an iterative heuristic MEC resource-allocation algorithm to make the offloading decision dynamically. Reference [36] optimizes the joint caching, offloading and time-allocation policy to minimize the weighted-sum energy consumption subject to the caching and deadline constraints. Resource management varies in the MEC system, but few of them jointly optimize computing, communication and caching of the offloading process, which is an all-sided optimization.
With the help of D2D cooperation, devices that cannot access the Internet choose one of the helper devices as a relay to execute and transmit tasks to the MEC server. Combined with edge caching and jointly optimized resource allocation, the model can improve resource utilization, shorten execution time and reduce energy consumption.

References

  1. Armbrust, M.; Fox, A.; Griffith, R.; Joseph, A.D.; Katz, R.; Konwinski, A.; Lee, G.; Patterson, D.; Rabkin, A.; Stoica, I.; et al. A view of cloud computing. Commun. ACM 2010, 53, 50–58.
  2. Zhang, Q.; Cheng, L.; Boutaba, R. Cloud computing: State-of-the-art and research challenges. J. Internet Serv. Appl. 2010, 1, 7–18.
  3. Abbas, N.; Zhang, Y.; Taherkordi, A.; Skeie, T. Mobile edge computing: A survey. IEEE Internet Things J. 2017, 5, 450–465.
  4. Mao, Y.; You, C.; Zhang, J.; Huang, K.; Letaief, K.B. A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tutorials 2017, 19, 2322–2358.
  5. Siriwardhana, Y.; Porambage, P.; Liyanage, M.; Ylianttila, M. A survey on mobile augmented reality with 5G mobile edge computing: Architectures, applications and technical aspects. IEEE Commun. Surv. Tutorials 2021, 23, 1160–1192.
  6. Wijethilaka, S.; Liyanage, M. Survey on network slicing for Internet of Things realization in 5G networks. IEEE Commun. Surv. Tutorials 2021, 23, 957–994.
  7. Gismalla, M.S.M.; Azmi, A.I.; Salim, M.R.B.; Abdullah, M.F.L.; Iqbal, F.; Mabrouk, W.A.; Othman, M.B.; Ashyap, A.Y.; Supa’at, A.S.M. Survey on device to device (D2D) communication for 5GB/6G networks: Concept, applications, challenges and future directions. IEEE Access 2022, 10, 30792–30821.
  8. Dai, X.; Xiao, Z.; Jiang, H.; Alazab, M.; Lui, J.C.; Dustdar, S.; Liu, J. Task co-offloading for d2d-assisted mobile edge computing in industrial Internet of things. IEEE Trans. Ind. Inform. 2022, 19, 480–490.
  9. Omidkar, A.; Khalili, A.; Nguyen, H.H.; Shafiei, H. Reinforcement-Learning-Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks. IEEE Internet Things J. 2022, 9, 16521–16531.
  10. Ouamri, M.A.; Barb, G.; Singh, D.; Adam, A.B.; Muthanna, M.; Li, X. Nonlinear Energy-Harvesting for D2D Networks Underlaying UAV with SWIPT Using MADQN. IEEE Commun. Lett. 2023, 27, 1804–1808.
  11. Pu, L.; Chen, X.; Xu, J.; Fu, X. D2D fogging: An energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration. IEEE J. Sel. Areas Commun. 2016, 34, 3887–3901.
  12. Wu, D.; Cai, Y.; Hu, R.Q.; Qian, Y. Dynamic distributed resource sharing for mobile D2D communications. IEEE Trans. Wirel. Commun. 2015, 14, 5417–5429.
  13. He, Y.; Ren, J.; Yu, G.; Cai, Y. D2D communications meet mobile edge computing for enhanced computation capacity in cellular networks. IEEE Trans. Wirel. Commun. 2019, 18, 1750–1763.
  14. Dai, Y.; Sheng, M.; Liu, J.; Cheng, N.; Shen, X.; Yang, Q. Joint mode selection and resource allocation for D2D-enabled NOMA cellular networks. IEEE Trans. Veh. Technol. 2019, 68, 6721–6733.
  15. Zeydan, E.; Bastug, E.; Bennis, M.; Kader, M.A.; Karatepe, I.A.; Er, A.S.; Debbah, M. Big data caching for networking: Moving from cloud to edge. IEEE Commun. Mag. 2016, 54, 36–42.
  16. He, S.; Huang, W.; Wang, J.; Ren, J.; Huang, Y.; Zhang, Y. Cache-enabled coordinated mobile edge network: Opportunities and challenges. IEEE Wirel. Commun. 2020, 27, 204–211.
  17. Jiang, W.; Feng, G.; Qin, S.; Yum, T.S.P.; Cao, G. Multi-agent reinforcement learning for efficient content caching in mobile D2D networks. IEEE Trans. Wirel. Commun. 2019, 18, 1610–1622.
  18. Qian, Y.; Wang, R.; Wu, J.; Tan, B.; Ren, H. Reinforcement learning-based optimal computing and caching in mobile edge network. IEEE J. Sel. Areas Commun. 2020, 38, 2343–2355.
  19. Xia, X.; Chen, F.; He, Q.; Grundy, J.; Abdelrazek, M.; Jin, H. Online collaborative data caching in edge computing. IEEE Trans. Parallel Distrib. Syst. 2020, 32, 281–294.
  20. Li, Q.; Zhang, Y.; Li, Y.; Xiao, Y.; Ge, X. Capacity-aware edge caching in fog computing networks. IEEE Trans. Veh. Technol. 2020, 69, 9244–9248.
  21. Huang, Y.; Song, X.; Ye, F.; Yang, Y.; Li, X. Fair and efficient caching algorithms and strategies for peer data sharing in pervasive edge computing environments. IEEE Trans. Mob. Comput. 2019, 19, 852–864.
  22. Xia, X.; Chen, F.; He, Q.; Cui, G.; Lai, P.; Abdelrazek, M.; Grundy, J.; Jin, H. Graph-based data caching optimization for edge computing. Future Gener. Comput. Syst. 2020, 113, 228–239.
  23. Safavat, S.; Sapavath, N.N.; Rawat, D.B. Recent advances in mobile edge computing and content caching. Digit. Commun. Netw. 2020, 6, 189–194.
  24. Vigneri, L.; Spyropoulos, T.; Barakat, C. Quality of experience-aware mobile edge caching through a vehicular cloud. In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, Miami, FL, USA, 21–25 November 2017; pp. 91–98.
  25. Yu, G.; Wu, J. Content caching based on mobility prediction and joint user Prefetch in Mobile edge networks. Peer-to-Peer Netw. Appl. 2020, 13, 1839–1852.
  26. Guo, J.; Song, B.; Chen, S.; Yu, F.R.; Du, X.; Guizani, M. Context-aware object detection for vehicular networks based on edge-cloud cooperation. IEEE Internet Things J. 2019, 7, 5783–5791.
  27. Dai, Y.; Xu, D.; Zhang, K.; Maharjan, S.; Zhang, Y. Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks. IEEE Trans. Veh. Technol. 2020, 69, 4312–4324.
  28. Wu, W.; Zhang, N.; Cheng, N.; Tang, Y.; Aldubaikhy, K.; Shen, X. Beef up mmWave dense cellular networks with D2D-assisted cooperative edge caching. IEEE Trans. Veh. Technol. 2019, 68, 3890–3904.
  29. Alkama, D.; Zenadji, S.; Ouamri, M.A.; Khireddine, A.; Azni, M. Performance of Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems in Tri-Sectorial Cell. In Proceedings of the 2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), Tunis, Tunisia, 26–28 October 2022; Volume 4, pp. 1–6.
  30. Sylia, Z.; Cédric, G.; Amine, O.M.; Abdelkrim, K. Resource allocation in a multi-carrier cell using scheduler algorithms. In Proceedings of the 2018 4th International Conference on Optimization and Applications (ICOA), Mohammedia, Morocco, 26–27 April 2018; pp. 1–5.
  31. You, C.; Huang, K.; Chae, H.; Kim, B.H. Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 2016, 16, 1397–1411.
  32. Ren, J.; Yu, G.; Cai, Y.; He, Y. Latency optimization for resource allocation in mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 2018, 17, 5506–5519.
  33. Tran, T.X.; Pompili, D. Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Trans. Veh. Technol. 2018, 68, 856–868.
  34. Wang, C.; Yu, F.R.; Liang, C.; Chen, Q.; Tang, L. Joint computation offloading and interference management in wireless cellular networks with mobile edge computing. IEEE Trans. Veh. Technol. 2017, 66, 7432–7445.
  35. Ning, Z.; Dong, P.; Kong, X.; Xia, F. A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things. IEEE Internet Things J. 2018, 6, 4804–4814.
  36. Wen, W.; Cui, Y.; Quek, T.Q.; Zheng, F.C.; Jin, S. Joint optimal software caching, computation offloading and communications resource allocation for mobile edge computing. IEEE Trans. Veh. Technol. 2020, 69, 7879–7894.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Computer Science, Software Engineering
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Jiaqi Ge
,
Gaochao Xu
,
Yang Zhang
,
Jianchao Lu
,
Haihua Chen
,
Xiangyu Meng
View Times: 243
Revisions: 2 times (View History)
Update Date: 10 Aug 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback