Abstract
With the development of the Internet of Things and the emergence of various computing paradigms, the use of social networks has become more diverse and data has exploded, making users more sensitive to the access delay of various new media when using social media. To meet the demand of massive data processing and users' access delay, edge-cloud computing—a new computing paradigm combining cloud computing and edge computing- starts to provide users with data storage and processing services. The popularity and convenience of smart devices, with hundreds of millions of users using social networking apps on their smart devices, has led to an explosion in the amount of data generated by the devices. However, in the edge-cloud environment, there is no trust mechanism between multilayer resource nodes. How to maintain the load balance of data storage to ensure the system performance becomes increasingly important. To solve the above problems, based on GP algorithm, a secure data placement model of edge-cloud computing is proposed under the constraints of ensuring user access delay and load balance. In this paper, real datasets are used for simulation experiments, and the experimental results show that the proposed algorithm has good performance.
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08 April 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10878-024-01161-7
References
Ali (2020) Aliyun Elastic Compute Service. https://www.aliyun.com/product/ecs?spm=5176.19720258.J_3207526240.33.229a76f4Jx6oaW. Accessed 22 Jan 2020
Alwakeel AM (2021) An overview of fog computing and edge computing security and privacy issues. Sensors 21(24):8226
Amazon (2019) Amazon S3[EB/OL]. http://aws.amazon.com/s3. Accessed 10 Dec 2019
Amin S (2020) SaaS-delivered encrypted traffic analytics with cisco stealthwatch cloud. https://blogs.cisco.com/security/saas-delivered-encrypted-traffic-analytics-with-cisco-stealthwatch-cloud?dtid=osscdc000283
Chen HH, Jin H, Wu SL (2015) Minimizing inter-server communications by exploiting self-similarity in online social networks. IEEE Trans Parallel Distrib Syst 27:1116–1130
Gu S, Guo D, Tang G, et al. (2019) HyEdge: optimal request scheduling in hybrid edge computing environment. J Latex Class Files 1–11. arXiv preprint arXiv:1909.06499
Hassan B, Askar S (2021) Survey on edge computing security. Int J Sci Bus 5(3):52–60
Inpander Oversea KOL (2021) Authoritative report! Global social media users will hit 4.5 billion in 2021. https://www.sohu.com/a/506466317_121169858. Accessed 9 Dec 2021
Khalajzadeh H, Yuan D, Grundy J, et al. (2016) Improving cloud-based online social network data placement and replication. In: Proceedings of the 2016 IEEE 9th international conference on cloud computing (CLOUD). IEEE, pp 678–685
Khalajzadeh H, Yuan D, Grundy J, et al. (2017) Cost-effective social network data placement and replication using graph-partitioning. In: Proceedings of the 2017 IEEE international conference on cognitive computing (ICCC). IEEE, pp 64–71
Li CL, Wang YP, Tang HL et al (2019a) Dynamic multi-objective optimized replica placement and migration strategies for saas applications in edge cloud. Futur Gener Comput Syst 100:921–937
Li CL, Bai JP, Tang JH (2019b) joint optimization of data placement and scheduling for improving user experience in edge computing. J Parallel Distrib Comput 125:93–105
Liu Q (2021) TikTok hits 1 billion monthly active users, putting it on par with Facebook-owned Instagram. https://baijiahao.baidu.com/s?id=1712234935419892055&wfr=spider&for=pc. Accessed 29 September 2021
Liu JB, Pan XF (2016) Minimizing Kirchhoffindex among graphs with a given vertex bipartiteness. Appl Math Comput 291:84–88
Liu JB, Pan XF, Yu L, Li D (2016) Complete characterization of bicyclic graphs with minimal Kirchhoff index. Discrete Appl Math 200:95–107
Liu JB, Bao Y, Zheng WT, Hayat S (2021) Network coherence analysis on a family of nested weighted n-polygon networks. Fractals 29(8):215–260
Liu JB, Zhang T, Wang YK, Lin WS (2022) The Kirchhoff index and spanning trees of Möbius/cylinder octagonal chain. Discret Appl Math 307:22–31
Somos Digital (2021) Overseas marketing data: Facebook statistics for 2021. https://zhuanlan.zhihu.com/p/358146956i?ivk_sa=1024320u. Accessed 25 Mar 2021
Wang SG, Zhao YL, Xu JL et al (2019) Edge server placement in mobile edge computing. J Parallel Distrib Comput 127:160–168
Wen S, Zhou W, Zhang J, Xiang Y, Zhou WL, Jia WJ (2012) Modeling propagation dynamics of social network worms. IEEE Trans Parallel Distrib Syst 24:1633–1643
Wu Y, Wu C, Li B et al (2015) Scaling social media applications into geo-distributed clouds. IEEE/ACM Trans Netw (TON) 23(3):689–702
Wu Q, Liu H, Zhang C, Fan Q, Li Z, Wang K (2019) Trajectory protection schemes based on a gravity mobility model in IoT. Electronics 8(148):1–19
Wu Q, Wan Z, Fan Q, Fan P, Wang J (2022) Velocity-adaptive access scheme for mec-assisted platooning networks: access fairness via data freshness. IEEE Internet Things J 9(6):4229–4244
Yang B, Chai WK, Xu Z et al (2018) Cost-efficient NFV-enabled mobile edge-cloud for low latency mobile applications. IEEE Trans Netw Serv Manag PP(99):1
Zhang L, Li XJ, Khalajzadeh H, et al. (2018) Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks. In: Proceedings of the 2018 IEEE 22nd international conference on computer supported cooperative work in design (CSCWD). IEEE, pp 110–115
Zhou JY, Fan JX, Wang J (2017) Towards traffic minimization for data placement in online social networks. Concurr Comput: Pract Exp 29(6):e3869
Zhu TX, Shi T, Li JZ, et al. (2018) Task scheduling in deadline-aware mobile edge computing systems. IEEE Internet Things J 6(3):4854–4866
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This paper is supported by the fund of Excellent Young Talents support Program of Anhui Province (gxyq2022135) and Natural Science Research Key project of Higher Education Department of Anhui Province (KJ2020A0783).
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Shi, W., Tang, Q. RETRACTED ARTICLE: Cost-optimized data placement strategy for social network with security awareness in edge-cloud computing environment. J Comb Optim 45, 22 (2023). https://doi.org/10.1007/s10878-022-00934-2
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DOI: https://doi.org/10.1007/s10878-022-00934-2