A comprehensive survey of recent proposed content replacement strategies for cooperative edge caching in IoT
Subject Areas : Majlesi Journal of Telecommunication Devices
1 -
Keywords: Internet of Things, Edge caching, Edge computing, Deep Reinforcement Learning, Federate learning,
Abstract :
The Internet of Things significantly increases the number of terminals and network traffic load, while its real-time applications require minimal latency to access the requested contents from data centers. Despite the processing and storage capabilities of base stations in 5G networks, the use of edge caching has proven to be an effective solution to reduce content access delay and repetitive traffic. This optimization of content transfer through the internet is crucial for maintaining the efficiency and performance of IoT applications. However, several challenges must be addressed. The limited storage resources, the constant changes in network nodes, and the dynamic patterns of content requests and user behavior pose fundamental challenges to content placement strategies. Developing an effective content placement strategy requires a comprehensive understanding of these factors and the ability to adapt to the evolving network environment.. In this paper, Challenges and issues facing content placement strategies in cooperate edge caching are described and recent researches in this field are reviewed, categorized, and explained comprehensively.
References:
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[1] L. Yang, Y. Chen, L. Li, and H. Jiang, Cooperative Caching and Delivery Algorithm Based on Content Access Patterns at Network Edge, vol. 278. Springer International Publishing, 2019.
[2] Z. Piao, M. Peng, Y. Liu, and M. Daneshmand, “Recent Advances of Edge Cache in Radio Access Networks for Internet of Things: Techniques, Performances, and Challenges,” IEEE Internet Things J., vol. PP, no. c, p. 1, 2018.
[3] X. Wang, M. Chen, T. Taleb, A. Ksentini, and V. C. M. Leung, “Cache in the air: Exploiting content caching and delivery techniques for 5G systems,” IEEE Commun. Mag., vol. 52, no. 2, pp. 131–139, 2014.
[4] G. Xu et al., “An Incremental Learning Based Edge Caching System : From Modeling to Evaluation,” vol. 8, pp. 12499–12509, 2020.
[5] F. Majidi, M. R. Khayyambashi and B. Barekatain, "HFDRL: An Intelligent Dynamic Cooperate Cashing Method Based on Hierarchical Federated Deep Reinforcement Learning in Edge-Enabled IoT," in IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1402-1413, 15 Jan.15, 2022, doi: 10.1109/JIOT.2021.3086623.
[6] R. Wang, M. Li, L. Peng, Y. Hu, M. M. Hassan, and A. Alelaiwi, “Cognitive multi-agent empowering mobile edge computing for resource caching and collaboration,” Futur. Gener. Comput. Syst., vol. 102, pp. 66–74, 2020.
[7] Z. Su, Y. Hui, Q. Xu, T. Yang, J. Liu, and Y. Jia, “An edge caching scheme to distribute content in vehicular networks,” IEEE Trans. Veh. Technol., vol. 67, no. 6, pp. 5346–5356, 2018.
[8] Z. Yu et al., “Federated Learning Based Proactive Content Caching in Edge Computing,” 2018 IEEE Glob. Commun. Conf. GLOBECOM 2018 - Proc., 2018.
[9] X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning,” IEEE Netw., vol. XX, pp. 1–10, 2019.
[10] Z. Zheng, L. Song, Z. Han, G. Y. Li, and H. V. Poor, “A stackelberg game approach to proactive caching in large-scale mobile edge networks,” IEEE Trans. Wirel. Commun., vol. 17, no. 8, pp. 5198–5211, 2018.
[11] S. Zhang, P. He, K. Suto, P. Yang, L. Zhao, and X. Shen, “Cooperative Edge Caching in User-Centric Clustered Mobile Networks,” IEEE Trans. Mob. Comput., vol. 17, no. 8, pp. 1791–1805, 2018.
[12] Y. M. Saputra, H. T. Dinh, D. Nguyen, and E. Dutkiewicz, “A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation,” IEEE Trans. Mob. Comput., pp. 1–1, 2019.
[13] Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, D. Niyato, and D. I. Kim, “Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks,”
[14] C. Wang, S. Wang, D. Li, X. Wang, X. Li, and V. C. M. Leung, “Q-learning based edge caching optimization for D2D enabled hierarchical wireless networks,” Proc. - 15th IEEE Int. Conf. Mob. Ad Hoc Sens. Syst. MASS 2018, pp. 55–63, 2018.
[15] N. Wang, G. Shen, S. K. Bose, and W. Shao, “Zone-Based Cooperative Content Caching and Delivery for Radio Access Network with Mobile Edge Computing,” IEEE Access, vol. 7, pp. 4031–4044, 2019.
[16] X. Li, X. Wang, P. J. Wan, Z. Han, and V. C. M. Leung, “Hierarchical edge caching in device-to-device aided mobile networks: Modeling, optimization, and design,” IEEE J. Sel. Areas Commun., vol. 36, no. 8, pp. 1768–1785, 2018.
[17] X. Zhao, P. Yuan, H. Li, and S. Tang, “Collaborative edge caching in context-aware device-to-device networks,” IEEE Trans. Veh. Technol., vol. 67, no. 10, pp. 9583–9596, 2018.
[18] X. He, K. Wang, and W. Xu, “in Edge-Enabled IoT,” IEEE Comput. Intell. Mag., vol. 14, no. NOVEMBER, pp. 10–20, 2019.
[19] G. Qiao, S. Leng, S. Maharjan, Y. Zhang, and S. Member, “Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks,” IEEE Internet Things J., vol. 7, no. 1, pp. 247–257, 2020.
[20] Y. Zhang, Y. Li, R. Wang, J. Lu, X. Ma, and M. Qiu, “PSAC: Proactive Sequence-aware Content Caching via Deep Learning at the Network Edge,” IEEE Trans. Netw. Sci. Eng., vol. 4697, no. c, pp. 1–1, 2020.
[21] W. Chien, H. Weng, and C. Lai, “Q-learning based collaborative cache allocation in mobile edge computing,” Futur. Gener. Comput. Syst., vol. 102, pp. 603–610, 2020.
[22] X. Wang, C. Wang, X. Li, V. C. M. Leung, and T. Taleb, “Federated Deep Reinforcement Learning for Internet of Things with Decentralized Cooperative Edge Caching,” IEEE Internet Things J., no. c, pp. 1–1, 2020.
[23] P. Wu, J. Li, L. Shi, M. Ding, K. Cai, and F. Yang, “Dynamic Content Update for Wireless Edge Caching via Deep Reinforcement Learning.”
[24] A. Mehrabi, M. Siekkinen, and A. Yla-Jaaski, “QoE-Traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming,” IEEE Access, vol. 6, pp. 52261–52276, 2018.
[25] E. Bastug, M. Bennis, and M. Debbah, “Living on the edge: The role of proactive caching in 5G wireless networks,” IEEE Commun. Mag., vol. 52, no. 8, pp. 82–89, 2014.
[26] M. F. Pervej, L. T. Tan, and R. Q. Hu, “Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks,” arXiv, 2020.
[27] N. Garg et al., “Online Content Popularity Prediction and Learning in Wireless Edge Caching,” vol. 68, no. 2, pp. 1087–1100, 2020.
[28] H. Pang, J. Liu, X. Fan, and L. Sun, “Toward Smart and Cooperative Edge Caching for 5G Networks: A Deep Learning Based Approach,” 2018 IEEE/ACM 26th Int. Symp. Qual. Serv. IWQoS 2018, 2019.
[29] J. Zheng et al., “Smart Edge Caching-Aided Partial Opportunistic Interference Alignment in HetNets,” Mob. Networks Appl., vol. 25, no. 5, pp. 1842–1850, 2020.
[30] M. C. Gursoy, C. Zhong, and S. Velipasalar, “Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching,” pp. 439–457, 2020.
[31] F. Wang, F. Wang, J. Liu, R. Shea, and L. Sun, “Intelligent Video Caching at Network Edge : A Multi-Agent Deep Reinforcement Learning Approach,” 2017.
[32] H. Brendan McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” Proc. 20th Int. Conf. Artif. Intell. Stat. AISTATS 2017, 2017.
[33] X. Sun and N. Ansari, “Dynamic Resource Caching in the IoT Application Layer for Smart Cities,” IEEE Internet Things J., vol. 5, no. 2, pp. 606–613, 2018.
[34] Z. Piao, M. Peng, Y. Liu, and M. Daneshmand, “Recent Advances of Edge Cache in Radio Access Networks for Internet of Things: Techniques, Performances, and Challenges,” IEEE Internet Things J., vol. 6, no. 1, pp. 1010–1028, 2019.
[35] L. Li, G. Zhao, and R. S. Blum, “A survey of caching techniques in cellular networks: Research issues and challenges in content placement and delivery strategies,” IEEE Commun. Surv. Tutorials, vol. 20, no. 3, pp. 1710–1732, 2018.
[36] J. Liu, B. Bai, J. Zhang, and K. B. Letaief, “Content caching at the wireless network edge: A distributed algorithm via belief propagation,” 2016 IEEE Int. Conf. Commun. ICC 2016, 2016.
[37] W. Jiang, G. Feng, S. Qin, and Y. Liu, “Multi-Agent Reinforcement Learning Based Cooperative Content Caching for Mobile Edge Networks,” IEEE Access, vol. 7, pp. 61856–61867, 2019.
[38] Z. Sang, S. Guo, Q. Wang, and Y. Wang, “GCS: Collaborative video cache management strategy in multi-access edge computing,” Ad Hoc Networks, vol. 117, 2021.
[39] S. Dutta and A. Narang, “Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks,” Big Data Princ. Paradig., pp. 389–415, 2016.
[40] X. Zhang and Q. Zhu, “Collaborative hierarchical caching over 5G edge computing mobile wireless networks,” IEEE Int. Conf. Commun., vol. 2018-May, pp. 1–6, 2018.
[41] D. Ren, X. Gui, K. Zhang, and J. Wu, “Hybrid collaborative caching in mobile edge networks: An analytical approach,” Comput. Networks, vol. 158, pp. 1–16, 2019.
[42] T. X. Tran, P. Pandey, A. Hajisami, and D. Pompili, “Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks,” 2017 13th Annu. Conf. Wirel. On-Demand Netw. Syst. Serv. WONS 2017 - Proc., pp. 165–172, 2017.
[43] Z. Chen, J. Lee, T. Q. S. Quek, and M. Kountouris, “Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks,” IEEE Trans. Wirel. Commun., vol. 16, no. 5, pp. 3401–3415, 2017.
[44] L. Chen and J. Xu, “Collaborative Service Caching for Edge Computing in Dense Small Cell Networks,” pp. 1–30, 2017.
[45] S. Wang, X. Zhang, K. Yang, L. Wang, and W. Wang, “Distributed Edge Caching Scheme Considering the Tradeoff Between the Diversity and Redundancy of Cached Content,” 2017.
[46] M. K. Somesula, R. R. Rout, and D. V. L. N. Somayajulu, “Contact duration-aware cooperative cache placement using genetic algorithm for mobile edge networks,” Comput. Networks, vol. 193, 2021.
[47] A. Sadeghi, F. Sheikholeslami, and G. B. Giannakis, “Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities,” IEEE J. Sel. Top. Signal Process., vol. 12, no. 1, pp. 180–190, 2018.
[48] Y. Dai, D. Xu, S. Maharjan, G. Qiao, and Y. Zhang, “Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles,” IEEE Wirel. Commun., vol. 26, no. 3, pp. 12–18, 2019.
[49] W. Shi et al., “LEAP: Learning-based smart edge with caching and prefetching for adaptive video streaming,” Proc. Int. Symp. Qual. Serv. IWQoS 2019, 2019.
[50] I. A. Elgendy, W. Z. Zhang, H. He, B. B. Gupta, and A. A. Abd El-Latif, “Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms,” Wirel. Networks, vol. 27, no. 3, pp. 2023–2038, 2021.
[51] K. Qi and C. Yang, “Popularity Prediction with Federated Learning for Proactive Caching at Wireless Edge,” IEEE Wirel. Commun. Netw. Conf. WCNC, vol. 2020-May, 2020.
