[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

An auto-scaling mechanism for cloud-based multimedia storage systems: a fuzzy-based elastic controller

  • 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS)
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Cloud computing is a new technology that is increasing in popularity day-by-day. One of the reasons for its popularity can be its elasticity feature. In other words, cloud computing considers the consumer’s resource capacity to be infinite, where the consumer can obtain the resources on-demand and increase or decrease the number of resources. Although various solutions for elasticity management have been developed so far, more work is needed to manage the elasticity of the cloud-based multimedia storage systems more effectively. Accordingly, this paper presents the Observe–Orient–Decide–Act (OODA) loop to improve the resource elasticity in cloud-based multimedia storage systems. In the proposed solution, elasticity management is performed using the OODA loop and fuzzy logic theory. Our simulation results demonstrate that the proposed solution reduces the read time, write time, response time by 7.2%, 6.9%, and 8.4%, respectively, compared with existing elastic cloud-based storage mechanisms.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

References

  1. Ai W, Li K, Lan S, Zhang F, Mei J, Li K, Buyya R (2016) On elasticity measurement in cloud computing. Sci Program 2016:1–13

    Google Scholar 

  2. Al-Dhuraibi Y, Zalila F, Djarallah N, Merle P (2018, March) Coordinating vertical elasticity of both containers and virtual machines

    Google Scholar 

  3. Arabnejad H, Pahl C, Jamshidi P, Estrada G (2017, May). A comparison of reinforcement learning techniques for fuzzy cloud auto-scaling. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (pp. 64-73). IEEE.

  4. Aslanpour MS, Toosi AN, Taheri J, Gaire R (2021) AutoScaleSim: A simulation toolkit for auto-scaling Web applications in clouds. Simulation Modelling Practice and Theory 108:102245. https://doi.org/10.1016/j.simpat.2020.102245

  5. Beloglazov A, Buyya R (2010) Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers MGC@ Middleware, 4.

  6. Beltrán M (2016) BECloud: a new approach to analyse elasticity enablers of cloud services. Futur Gener Comput Syst 64:39–49

    Article  Google Scholar 

  7. Bowers KD, Juels A Oprea A (2009, November) HAIL: a high-availability and integrity layer for cloud storage. In Proceedings of the 16th ACM conference on Computer and communications security (pp. 187–198).

  8. Aslanpour MS, Toosi AN, Gaire R, Cheema MA (2020) Auto-scaling of web applications in clouds: A tail latency evaluation. In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) (pp. 186–195). IEEE. https://doi.org/10.1109/UCC48980.2020.00037

  9. Aslanpour MS, Dashti SE (2016) SLA-aware resource allocation for application service providers in the cloud. In 2016 Second International Conference on Web Research (ICWR) (pp. 31–42). IEEE. https://doi.org/10.1109/ICWR.2016.7498443

  10. Cardellini V, Grbac TG, Nardelli M, Tanković N, Truong HL (2018) Qos-based elasticity for service chains in distributed edge cloud environments. In Autonomous Control for a Reliable Internet of Services (pp. 182-211). Springer, Cham.

  11. Chen L, Qiu M, Song J, Xiong Z, Hassan H (2018) E2fs: an elastic storage system for cloud computing. J Supercomput 74(3):1045–1060

    Article  Google Scholar 

  12. Chiesa G, Di Vita D, Ghadirzadeh A, Herrera AHM, Rodriguez JCL (2020) A fuzzy-logic IoT lighting and shading control system for smart buildings. Autom Constr 120:103397

    Article  Google Scholar 

  13. Chitra K, Vennila C (2020) A novel patch selection technique in ANN B-spline Bayesian hyperprior interpolation VLSI architecture using fuzzy logic for highspeed satellite image processing. Journal of Ambient Intelligence and Humanized Computing, pp.1-14.

  14. Cidon Cidon A, Escriva R, Katti S, Rosenblum M, Sirer EG (2015) Tiered replication: A cost-effective alternative to full cluster geo-replication. In 2015 {USENIX} Annual Technical Conference ({USENIX}{ATC} 15) (pp. 31–43).

  15. Franco JD, Ramirez-delReal TA, Villanueva D, Gárate-García A, Armenta-Medina D (2020) Monitoring of Ocimum basilicum seeds growth with image processing and fuzzy logic techniques based on Cloudino-IoT and FIWARE platforms. Comput Electron Agric 173:105389

    Article  Google Scholar 

  16. Galante G, de Bona LCE (2012, November) A survey on cloud computing elasticity. In 2012 IEEE Fifth International Conference on Utility and Cloud Computing (pp. 263-270). IEEE.

  17. Gueye SMK, De Palma N, Rutten É, Tchana A, Berthier N (2014) Coordinating self-sizing and self-repair managers for multi-tier systems. Futur Gener Comput Syst 35:14–26

    Article  Google Scholar 

  18. Harter T, Borthakur D, Dong S, Aiyer A, Tang L, Arpaci-Dusseau AC, Arpaci-Dusseau RH (2014P) Analysis of {HDFS} under HBase: a Facebook messages case study. In 12th {USENIX} Conference on File and Storage Technologies ({FAST} 14) (pp. 199-212).

  19. Hosamani N, Albur N, Yaji P, Mulla MM, Narayan DG (2020, July) Elastic provisioning of Hadoop clusters on OpenStack private cloud. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.

  20. Jamshidi P, Ahmad A, Pahl C (2014, June) Autonomic resource provisioning for cloud-based software. In Proceedings of the 9th international symposium on software engineering for adaptive and self-managing systems (pp. 95-104).

  21. Jannapureddy R, Vien QT, Shah P, Trestian R (2019) An auto-scaling framework for analyzing big data in the cloud environment. Applied Sciences 9(7):1417

    Article  Google Scholar 

  22. Kaur PD, Chana I (2014) A resource elasticity framework for QoS-aware execution of cloud applications. Futur Gener Comput Syst 37:14–25

    Article  Google Scholar 

  23. Lehrig S, Sanders R, Brataas G, Cecowski M, Ivanšek S, Polutnik J (2018) CloudStore—towards scalability, elasticity, and efficiency benchmarking and analysis in cloud computing. Futur Gener Comput Syst 78:115–126

    Article  Google Scholar 

  24. Li K (2017) Quantitative modeling and analytical calculation of elasticity in cloud computing. IEEE Transactions on Cloud Computing.

    Google Scholar 

  25. Liu Y, Gureya D, Al-Shishtawy A, Vlassov V (2017) OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services. Clust Comput 20(3):1977–1994

    Article  Google Scholar 

  26. Lytvyn V, Dosyn D, Vysotska V, Hryhorovych A (2020, August) Method of ontology 45. Use in OODA. In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP) (pp. 409-413). IEEE.

  27. Maghsoudloo M, Khoshavi N (2020) Elastic HDFS: interconnected distributed architecture for availability–scalability enhancement of large-scale cloud storages. J Supercomput 76(1):174–203

    Article  Google Scholar 

  28. Marcus LJ, McNulty EJ, Flynn LB, Henderson JM, Neffenger PV, Serino R, Trenholm J (2020) The POP-DOC loop: a continuous process for situational awareness and situational action. Ind Mark Manag 88:272–277

    Article  Google Scholar 

  29. Meana-Llorián D, García CG, G-bustelo BCP, Lovelle JMC, Garcia-Fernandez N (2017) IoFClime: the fuzzy logic and the internet of things to control indoor temperature regarding the outdoor ambient conditions. Future Generation Computer Systems 76:275–284

    Article  Google Scholar 

  30. Mirzakhanov VE (2020) Value of fuzzy logic for data mining and machine learning: a case study. Expert Syst Appl 162:113781

    Article  Google Scholar 

  31. Newcombe C, Rath T, Zhang F, Munteanu B, Brooker M, Deardeuff M (2015) How Amazon web services uses formal methods. Commun ACM 58(4):66–73

    Article  Google Scholar 

  32. Qureshi NMF, Siddiqui IF, Unar MA, Uqaili MA, Nam CS, Shin DR, Kim J, Bashir AK, Abbas A (2019) An aggregate MapReduce data block placement strategy for wireless IoT edge nodes in smart grid. Wirel Pers Commun 106(4):2225–2236

    Article  Google Scholar 

  33. Révay M, Líška M (2017, October) OODA loop in command & control systems. In 2017 Communication and Information Technologies (KIT) (pp. 1-4). IEEE.

  34. Ghobaei-Arani M, Souri A, Baker T, Hussien A (2019) ControCity: an autonomous approach for controlling elasticity using buffer Management in Cloud Computing Environment. IEEE Access 7:106912–106924. https://doi.org/10.1109/ACCESS.2019.2932462

  35. Serrano D, Bouchenak S, Kouki Y, Ledoux T, Lejeune J, Sopena J, Arantes L, Sens P (2013, May). Towards qos-oriented SLA guarantees for online cloud services. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (pp. 50-57). IEEE.

  36. Sharmila S, Vijayarani S (2021) Association rule mining using fuzzy logic and whale optimization algorithm. Soft Comput 25(2):1431–1446

    Article  Google Scholar 

  37. Shi Y, Dong M, Zhang W, Liu L, Zheng Y, Cui L, Zhang J (2020) AdaptScale: an adaptive data scaling controller for improving the multiple performance requirements in clouds. Futur Gener Comput Syst 105:814–823

    Article  Google Scholar 

  38. Sivashakthi T, Prabakaran N (2013) A survey on storage techniques in cloud computing. International Journal of Emerging Technology and Advanced Engineering 3(12):125–128

    Google Scholar 

  39. Szalay M, Matray P, Toka L (2020, November) AnnaBellaDB: key-value store made cloud native. In 2020 16th International Conference on Network and Service Management (CNSM) (pp. 1-5). IEEE.

  40. Wang H, Varman P (2014) Balancing fairness and efficiency in tiered storage systems with bottleneck-aware allocation. In 12th {USENIX} Conference on File and Storage Technologies ({FAST} 14) (pp. 229-242).

  41. Wanke P, Falcão BB (2017) Cargo allocation in Brazilian ports: an analysis through fuzzy logic and social networks. J Transp Geogr 60:33–46

    Article  Google Scholar 

  42. Wu T, Liu X, Liu F (2018) An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information. Inf Sci 432:392–410

    Article  MathSciNet  Google Scholar 

  43. Wu C, Sreekanti V, Hellerstein JM (2020) Autoscaling tiered cloud storage in anna. The VLDB Journal:1–19

  44. Xu L, Cipar J, Krevat E, Tumanov A, Gupta N, Kozuch MA, Ganger GR (2014) Springfs: bridging agility and performance in elastic distributed storage. In 12th {USENIX} Conference on File and Storage Technologies ({FAST} 14) (pp. 243-255).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mostafa Ghobaei-Arani.

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

Ghobaei-Arani, M., Rezaei, M. & Souri, A. An auto-scaling mechanism for cloud-based multimedia storage systems: a fuzzy-based elastic controller. Multimed Tools Appl 81, 34501–34523 (2022). https://doi.org/10.1007/s11042-021-11021-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11021-9

Keywords

Navigation