Skip to main content
Log in

Optimization of heat-based cache replacement in edge computing system

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With high-speed development of smart devices, abundant data are generated at the edge of the network. Edge computing has three characteristics: low response delay, high network traffic and low backhaul link pressure so as to process tons of data. Nevertheless, the latency of cloud services faces huge challenges due to the increasing requirements for timely content delivery and real-time user interaction. In order to hide the delay of user requirement, a cache prefetching strategy is proposed based on UCBM algorithm. The Markov chain can classify user behaviors and the probability of access files for the certain users can be calculated by Bayes network. Then, the next task of user access can be predicted. This model obviously improves prefetched file accuracy. In this paper, a cache replacement policy is proposed based on FHPA algorithm, which takes full advantage of the limited edge device space. Considering the file heat, the probability of the re-accessed cache file is evaluated. If the cache file is the smallest re-accessed probability, it will be evicted from cache. In a campus network, an edge computing environment is built for performance evaluation of our algorithms which significantly outperforms benchmark algorithm.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Li Chunlin, Bai Jingpan, Tang JiangHang (2019) Joint optimization of data placement and scheduling for improving user experience in edge computing. J Parallel Distrib Comput 125:93–105

    Article  Google Scholar 

  2. Sonmez C, Ozgovde A, Ersoy C (2017) EdgeCloudSim: an environment for performance evaluation of Edge Computing systems. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC). pp 39–44

  3. Jiang Bo, Nain Philippe, Towsley Don (2017) LRU cache under stationary requests. ACM SIGMETRICS Performance Evaluation Review. 45(2):24–26

    Article  Google Scholar 

  4. Chunlin Li, Jianhang Tang, Tang Hengliang, Youlong Luo (2019) Collaborative cache allocation and task scheduling for data-intensive applications in edge computing. Future Gener Comput Syst 95:249–264

    Article  Google Scholar 

  5. Wang S, Zhang X, Zhang Y et al (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5:6757–6779

    Article  Google Scholar 

  6. Li Chunlin, Bai Jingpan, Yi Chen, Luo Youlong (2020) Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system. Inf Sci 516:33–55

    Article  MathSciNet  Google Scholar 

  7. Chunlin L, Wang C, Tang H, Luo Y (2019) Scalable and dynamic replica consistency maintenance for edge-cloud system. Future Gener Comput Syst 101:590–604

    Article  Google Scholar 

  8. Shi W, Cao J, Zhang Q et al (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  9. Liu H, Eldarrat F, Alqahtani H et al (2018) Mobile edge computing system: architectures, challenges, and approaches. IEEE Syst J 12(3):2495–2508

    Article  Google Scholar 

  10. Ceselli A, Premoli M, Secci S (2017) Mobile edge cloud network design optimization. IEEE/ACM Trans Netw (TON). 25(3):1818–1831

    Article  Google Scholar 

  11. Du B, Huang R, Xie Z et al (2018) KID model-driven things-edge-cloud computing paradigm for traffic data as a service. IEEE Netw 32(1):34–41

    Article  Google Scholar 

  12. Masip-Bruin X, Marin-Tordera E, Jukan A et al (2018) Managing resources continuity from the edge to the cloud: architecture and performance. Future Gener Comput Syst 79:777–785

    Article  Google Scholar 

  13. Alkassab N, Huang CT, Chen Y et al (2017) Benefits and schemes of prefetching from cloud to fog networks. In: 2017 IEEE 6th International Conference on Cloud Networking (CloudNet). IEEE. pp 1–5

  14. Baştuğ E, Bennis M, Debbah M (2014) Living on the edge: the role of proactive caching in 5G wireless networks. IEEE Commun Mag 52(8):82–89

    Article  Google Scholar 

  15. Baştuğ E, Bennis M, Zeydan E et al (2015) Big data meets telcos: a proactive caching perspective. J Commun Netw 17(6):549–557

    Article  Google Scholar 

  16. Gu J, Wang W, Huang A et al (2013) Proactive storage at caching-enable base stations in cellular networks. In: 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, pp 1543–1547

  17. Wang Y, Liu X, Chu D et al (2015) EarlyBird: mobile prefetching of social network feeds via content preference mining and usage pattern analysis. In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. ACM. pp 67–76

  18. Hu W, Jin Y, Wen Y et al (2017) Towards Wi-Fi AP-Assisted content prefetching for on-demand tv series: a learning-based approach. IEEE Trans Circuits Syst Video Technol. arXiv preprint arXiv:1703.03530

  19. Zhang X, Zhu Q. Distributed mobile devices caching over edge computing wireless networks. In: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, pp 127–132

  20. Zhang G, Li Y, Lin T (2013) Caching in information centric networking: a survey. Comput Netw 57(16):3128–3141

    Article  Google Scholar 

  21. Zhang M, Luo H, Zhang H (2015) A survey of caching mechanisms in information-centric networking. IEEE Commun Surv Tutor 17(3):1473–1499

    Article  Google Scholar 

  22. Wang X, Chen M, Taleb T et al (2014) Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2):131–139

    Article  Google Scholar 

  23. Tran TX, Pandey P, Hajisami A et al (2017) Collaborative multi-bitrate video caching and processing in mobile-edge computing networks. In: 2017 13th Annual Conference On Wireless On-Demand Network Systems and Services (WONS). pp 165–172

  24. Maddah-Ali MA, Niesen U (2015) Decentralized coded caching attains order-optimal memory-rate tradeoff. IEEE/ACM Trans Netw (TON) 23(4):1029–1040

    Article  Google Scholar 

  25. Pallis George, Vakali Athena et al (2008) A clustering-based prefetching scheme on a Web cache environment. Comput Electr Eng 34(4):309–323

    Article  Google Scholar 

  26. Lee MC, Leu FY et al (2014) Cache replacement algorithms for YouTube. In: 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pp 734–750

  27. Fofack NC, Nain P et al (2014) Performance evaluation of hierarchical TTL-based cache networks. Comput Netw 65:212–231

    Article  Google Scholar 

  28. Wu X, Xu H et al (2015) Web cache replacement strategy based-on reference degree. In: IEEE International Conference on Smart City, pp 209–212

  29. Benadit PJ, Francis FS (2015) Improving the performance of a proxy cache using very fast decision tree classifier. Procedia Comput Sci 48:304–312

    Article  Google Scholar 

  30. Tian G, Liebelt M (2014) An effectiveness-based adaptive cache replacement policy. Elsevier BV 38(1):98–111

    Google Scholar 

  31. Kalghoum A, Gammar SM et al (2018) Towards a novel cache replacement strategy for named data networking based on software defined networking. Comput Electr Eng 66:98–113

    Article  Google Scholar 

  32. Chang HP, Chiang CP et al (2016) An adaptive buffer cache management scheme. In: 2016 International Computer Symposium (ICS). IEEE, pp 124–127

  33. Du JH, Gao SW, Lv JH et al (2018) A web cache replacement strategy for safety-critical systems. Tehnicki Vjesnik-Technical Gazette 25(3):820–830

    Google Scholar 

  34. Ammar HB, Chellouche SA et al (2017) A Markov chain-based Approximation of CCN caching Systems. In: 2017 IEEE Symposium on Computers And Communications (ISCC), pp. 327-332

  35. Zheng Y, Ling D, Wang YW et al (2017) Model quality evaluation in semiconductor manufacturing process With EWMA run-to-run control. IEEE Trans Semicond Manuf 30(1):8–16

    Article  Google Scholar 

  36. Li Chunlin, Song Mingyang, Shaofeng Du et al (2020) Adaptive priority-based cache replacement and prediction-based cache prefetching in edge computing environment. J Netw Comput Appl 165:1–21

    Google Scholar 

Download references

Acknowledgements

The work was supported by Application Foundation Frontier Project of Wuhan (No. 2018010401011290), Open Fund of Artificial intelligence key laboratory of Sichuan province. Any opinions, findings and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chunlin Li.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Luo, Y. & Li, C. Optimization of heat-based cache replacement in edge computing system. J Supercomput 77, 2268–2301 (2021). https://doi.org/10.1007/s11227-020-03356-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03356-1

Keywords

Navigation