Advertisement

ProRec: a unified content caching and replacement framework for mobile edge computing

  • 26 Accesses

Abstract

In this paper, we investigate the content deployment problem from precaching and device-to-device communication perspectives. In the precaching stage, contents are prefetched and stored in edge nodes to be quickly provided to end users. In the device-to-device communication process, intermediate nodes face a dilemma in deciding whether to cache contents coming from or going to neighboring nodes to accelerate the content delivery. We call the former proactive caching and the latter reactive caching. We then design ProRec, a unified caching framework, by jointly considering the two cases with the goal of maximizing the content hit ratio. ProRec first addresses the optimization problem using the method of Lagrangian multipliers and obtains a general solution to the optimal content copies. Second, a greedy solution, proven to achieve the optimum with a probability of at least \(1-1/e\), is used to cache and replace contents. Finally, an edge computing simulation platform that includes real and synthetic traces is built as a case study to verify the effectiveness of ProRec. The numerical results show that it simultaneously improves the cache hit ratio and content delivery delay.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. 1.

    Throughout this paper, we will use the terms file, content and packet interchangeably.

  2. 2.

    For the variable-sized file, they can be separated into multiple unit segments, each of which can be treated as a separate fragment and cached independently of each other. We assume that each segment has a fixed length b, and each segment can belong to a portion of a cacheable video file, audio or picture. In this case, each SBS can cache \(b_1/b\) segments/files [37, 38].

  3. 3.

    A similar assumption can be found in the recent work [51].

References

  1. 1.

    Index, C. V. N. (2019). Global mobile data traffic forecast update, 2017–2022 white paper. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html.

  2. 2.

    Yuan, P., Cai, Y., Huang, X., Tang, S., & Zhao, X. (2019). Collaboration improves the capacity of mobile edge computing. IEEE Internet of Things Journal, 6(6), 10610–10619.

  3. 3.

    Roman, R., Lopez, J., & Mambo, M. (2016). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680–698.

  4. 4.

    Sodhro, A. H., Luo, Z., Sangaiah, A. K., & Baik, S. W. (2019). Mobile edge computing based qos optimization in medical healthcare applications. International Journal of Information Management, 45, 308–318.

  5. 5.

    Wang, S., Urgaonkar, R., Zafer, M., He, T., Chan, K., & Leung, K. K. (2019). Dynamic service migration in mobile edge computing based on markov decision process. IEEE/ACM Transactions on Networking, 27(3), 1272–1288.

  6. 6.

    Poularakis, K., Llorca, J., Tulino, A. M., Taylor, I., & Tassiulas, L. (2019). Joint service placement and request routing in multi-cell mobile edge computing networks. In IEEE INFOCOM 2019-IEEE conference on computer communications (pp. 10–18). IEEE.

  7. 7.

    Yuan, P., & Liu, P. (2015). Data fusion prolongs the lifetime of mobile sensing networks. Journal of Network and Computer Applications, 49, 51–59.

  8. 8.

    Zhao, D., Li, X.-Y., & Ma, H. (2016). Budget-feasible online incentive mechanisms for crowdsourcing tasks truthfully. IEEE/ACM Transactions on Networking (TON), 24(2), 647–661.

  9. 9.

    Xu, C., Feng, J., Zhou, Z., Wu, J., & Perera, C. (2017). Cross-layer optimization for cooperative content distribution in multihop device-to-device networks. IEEE Internet of Things Journal, 6(1), 278–287.

  10. 10.

    Wang, R., Zhang, J., Song, S., & Letaief, K. B. (2018). Exploiting mobility in cache-assisted d2d networks: Performance analysis and optimization. IEEE Transactions on Wireless Communications, 17(8), 5592–5605.

  11. 11.

    Quer, G., Pappalardo, I., Rao, B. D., & Zorzi, M. (2018). Proactive caching strategies in heterogeneous networks with device-to-device communications. IEEE Transactions on Wireless Communications, 17(8), 5270–5281.

  12. 12.

    Qiu, L., & Cao, G. (2019). Popularity-aware caching increases the capacity of wireless networks. IEEE Transactions on Mobile Computing, 19(1), 173–187.

  13. 13.

    Wang, J. M. & Bensaou, B. (2012). Progressive caching in ccn. In 2012 IEEE global communications conference (GLOBECOM) (pp. 2727–2732). IEEE.

  14. 14.

    Liu, Z., Ji, Y., Jiang, X., & Tanaka, Y. (2016). User-behavior driven video caching in content centric network. In Proceedings of the 3rd ACM conference on information-centric networking (pp. 197–198). ACM.

  15. 15.

    Ammar, H. B., Chellouche, S. A., Aoul, & Y. H. (2017). A markov chain-based approximation of ccn caching systems. In 2017 IEEE symposium on computers and communications (ISCC) (pp. 327–332). IEEE.

  16. 16.

    Mokhtarian, K., & Jacobsen, H.-A. (2014). Caching in video cdns: Building strong lines of defense. In Proceedings of the ninth European conference on computer systems (p. 13). ACM.

  17. 17.

    Mokhtarian, K., & Jacobsen, H.-A. (2017). Flexible caching algorithms for video content distribution networks. IEEE/ACM Transactions on Networking (TON), 25(2), 1062–1075.

  18. 18.

    Duan, J., Xing, Y., Tian, R., Zhao, G., Zeng, S., Liu, Y., et al. (2018). Scdn: A novel software-driven cdn for better content pricing and caching. IEEE Communications Letters, 22(4), 704–707.

  19. 19.

    Dehghan, M., Seetharam, A., Jiang, B., He, T., Salonidis, T., Kurose, J., Towsley, D., & Sitaraman, R. (2015). On the complexity of optimal routing and content caching in heterogeneous networks. In 2015 IEEE conference on computer communications (INFOCOM) (pp. 936–944). IEEE.

  20. 20.

    Cui, Y., Jiang, D., & Wu, Y. (2016). Analysis and optimization of caching and multicasting in large-scale cache-enabled wireless networks. IEEE Transactions on Wireless Communications, 15(7), 5101–5112.

  21. 21.

    Kwak, J., Kim, Y., Le, L. B., & Chong, S. (2018). Hybrid content caching in 5g wireless networks: Cloud versus edge caching. IEEE Transactions on Wireless Communications, 17(5), 3030–3045.

  22. 22.

    Sun, Y., Chen, Z., & Liu, H. (2016). Delay analysis and optimization in cache-enabled multi-cell cooperative networks. In Global communications conference (GLOBECOM), 2016 IEEE (pp. 1–7). IEEE.

  23. 23.

    Golrezaei, N., Molisch, A. F., Dimakis, A. G., & Caire, G. (2013). Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution. IEEE Communications Magazine, 51(4), 142–149.

  24. 24.

    Ao, W. C., & Psounis, K. (2015). Distributed caching and small cell cooperation for fast content delivery. In Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing (pp. 127–136.). ACM.

  25. 25.

    Bharath, B., Nagananda, K. G., & Poor, H. V. (2016). A learning-based approach to caching in heterogenous small cell networks. IEEE Transactions on Communications, 64(4), 1674–1686.

  26. 26.

    Golrezaei, N., Mansourifard, P., Molisch, A. F., & Dimakis, A. G. (2014). Base-station assisted device-to-device communications for high-throughput wireless video networks. IEEE Transactions on Wireless Communications, 13(7), 3665–3676.

  27. 27.

    Guo, Y., Duan, L., & Zhang, R. (2017). Cooperative local caching under heterogeneous file preferences. IEEE Transactions on Communications, 65(1), 444–457.

  28. 28.

    Malak, D., Al-Shalash, M., & Andrews, J. G. (2016). Optimizing content caching to maximize the density of successful receptions in device-to-device networking. IEEE Transactions on Communications, 64(10), 4365–4380.

  29. 29.

    Malak, D., & Al-Shalash, M. (2014). Optimal caching for device-to-device content distribution in 5g networks. In Globecom workshops (GC Wkshps), 2014 (pp. 863–868). IEEE.

  30. 30.

    Fan, L., Dong, Z., & Yuan, P. (2017). The capacity of device-to-device communication underlaying cellular networks with relay links. IEEE Access, 5, 16840–16846.

  31. 31.

    Zhao, X., Yuan, P., Chen, Y., & Chen, P. (2017). Femtocaching assisted multi-source d2d content delivery in cellular networks. EURASIP Journal on Wireless Communications and Networking, 2017(1), 125.

  32. 32.

    Ji, M., Caire, G., & Molisch, A. F. (2016). Wireless device-to-device caching networks: Basic principles and system performance. IEEE Journal on Selected Areas in Communications, 1(34), 176–189.

  33. 33.

    Xu, X., & Tao, M. (2017). Modeling, analysis, and optimization of coded caching in small-cell networks. IEEE Transactions on Communications, 65(8), 3415–3428.

  34. 34.

    Blaszczyszyn, B., & Giovanidis, A. (2015). Optimal geographic caching in cellular networks. In 2015 IEEE international conference on communications (ICC) (pp. 3358–3363). IEEE.

  35. 35.

    Baştuğ, E., Bennis, M., Zeydan, E., Kader, M. A., Karatepe, I. A., Er, A. S., et al. (2015). Big data meets telcos: A proactive caching perspective. Journal of Communications and Networks, 17(6), 549–557.

  36. 36.

    Zhao, X., Yuan, P., Tang, S., et al. (2018). Collaborative edge caching in context-aware device-to-device networks. IEEE Transactions on Vehicular Technology, 67(10), 9583–9596.

  37. 37.

    Liu, B., Firoiu, V., Kurose, J., Leung, M., & Nanda, S. (2014). Capacity of cache enabled content distribution wireless ad hoc networks. In 2014 IEEE 11th international conference on mobile ad hoc and sensor systems (pp. 309–317). IEEE.

  38. 38.

    Gitzenis, S., Paschos, G. S., & Tassiulas, L. (2012). Asymptotic laws for joint content replication and delivery in wireless networks. IEEE Transactions on Information Theory, 59(5), 2760–2776.

  39. 39.

    Liu, J., Wan, J., Zeng, B., Wang, Q., Song, H., & Qiu, M. (2017). A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Communications Magazine, 55(7), 94–100.

  40. 40.

    Yang, C., Yao, Y., Chen, Z., & Xia, B. (2016). Analysis on cache-enabled wireless heterogeneous networks. IEEE Transactions on Wireless Communications, 15(1), 131–145.

  41. 41.

    Lee, N., Lin, X., Andrews, J. G., & Heath, R. W. (2014). Power control for d2d underlaid cellular networks: Modeling, algorithms, and analysis. IEEE Journal on Selected Areas in Communications, 33(1), 1–13.

  42. 42.

    Bassoy, S., Farooq, H., Imran, M. A., & Imran, A. (2017). Coordinated multi-point clustering schemes: A survey. IEEE Communications Surveys and Tutorials, 19(2), 743–764.

  43. 43.

    Fricker, C., Robert, P., & Roberts, J. (2012). A versatile and accurate approximation for lru cache performance. In 2012 24th international teletraffic congress (ITC 24) (pp. 1–8). IEEE.

  44. 44.

    Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions - i. Mathematical programming, 14(1), 265–294.

  45. 45.

    Yuan, P., & Song, M. (2018). Monica: One simulator for mobile opportunistic networks, EAI. https://doi.org/10.4108/eai.21-6-2018.2276563.

  46. 46.

    Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S. J., & Chong, S. (2011). On the levy-walk nature of human mobility. IEEE/ACM Transactions on Networking (TON), 19(3), 630–643.

  47. 47.

    Lee, K., Hong, S., Kim, S. J., Rhee, I., & Chong, S. (2012). Slaw: Self-similar least-action human walk. IEEE/ACM Transactions on Networking (TON), 20(2), 515–529.

  48. 48.

    Wen, W., Cui, Y., Zheng, F.-C., Jin, S., & Jiang, Y. (2018). Random caching based cooperative transmission in heterogeneous wireless networks. IEEE Transactions on Communications, 66(7), 2809–2825.

  49. 49.

    Gao, W., Cao, G., Iyengar, A., & Srivatsa, M. (2014). Cooperative caching for efficient data access in disruption tolerant networks. IEEE Transactions on Mobile Computing, 13(3), 611–625.

  50. 50.

    Lee, D., Choi, J., Kim, J.-H., Noh, S. H., Min, S. L., Cho, Y., et al. (2001). Lrfu: A spectrum of policies that subsumes the least recently used and least frequently used policies. IEEE Transactions on Computers, 12, 1352–1361.

  51. 51.

    Liu, Q., Huang, S., Opadere, J. & Han, T. (2018). An edge network orchestrator for mobile augmented reality. In IEEE INFOCOM 2018-IEEE conference on computer communications (pp. 756–764). IEEE.

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U1804164, 61902112 and U1404602, in part by the Science and Technology Foundation of Henan Educational Committee under Grants 19A510015, 20A520019 and 20A520020.

Author information

Correspondence to Peiyan Yuan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yuan, P., Cai, Y., Liu, Y. et al. ProRec: a unified content caching and replacement framework for mobile edge computing. Wireless Netw (2020) doi:10.1007/s11276-020-02248-9

Download citation

Keywords

  • Proactive caching
  • Reactive caching
  • Cache hit ratio
  • Edge computing
  • D2D communication