Skip to main content
SpringerLink
Log in

Autonomous multimedia cluster computing based on Cooperative Cognition data behavior measurement under multi cloud computing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In order to meet the needs of multimedia communication in multi cloud environment and improve the experience quality of mobile multimedia users, based on multi cloud computing, based on Cooperative cognitive data behavior measurement, this paper proposes an autonomous multimedia cluster computer system and its architecture. First of all, dispersed edge clouds are distributed and integrated, and cooperate to provide multimedia storage and computing functions. In cloudy environment, a hierarchical access service point is designed between edge cloud and core cloud. On this basis, a multimedia cluster computing system suitable for cloudy environment is built. Secondly, a mulch-dimensional mapping mechanism is built between the link management interface array and the task scheduling array in the edge cloud array. Mulch-dimensional multimedia data and real-time task scheduling cooperation cognition, and core cloud are used to interact with DP vectors with dedicated channels. On this basis, we propose an autonomous computer system based on Cooperative Cognition and multimedia data behavior measure. Finally, it is analyzed by three groups of experiments. The resource utilization of the multimedia cluster computing system, the behavior measurement accuracy based on the cooperative cognitive multimedia data behavior measure and the performance of the proposed autonomous multimedia cluster computing (AMC-CCC) in large-scale real-time multimedia communication applications. The results show that the proposed AMC-CCC mechanism has excellent performance in multimedia QoS, resource management and data behavior measurement.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Anarado I, Anam MA, Verdicchio F et al (2017) Mitigating silent data corruptions in integer matrix products: toward reliable multimedia computing on unreliable hardware[J]. IEEE Trans Circuits Syst Video Technol (99):1–1

  2. Cui P, Zhu W, Chua TS et al (2016) Social-sensed multimedia computing[J]. IEEE Multimedia 23(1):92–96

    Article  Google Scholar 

  3. Durga S, Mohan S, Peter JD (2018) A two-stage queue model for context-aware task scheduling in mobile multimedia cloud environments[M]. Advances in Big Data and Cloud Computing.

  4. Faber M, Bixler R, D’Mello SK (2017) An automated behavioral measure of mind wandering during computerized reading[J]. Behav Res Methods 50(1):1–17

    Google Scholar 

  5. Governo F, Teixeira AAC, Brochado AM (2017) Social multimedia computing: an emerging area of research and business for films[J]. J Creative Commun 12(1):1–17

    Article  Google Scholar 

  6. Hajibandeh N (2016) Sheikh-El-Eslami M K, Aminnejad S, et al. resemblance measurement of electricity market behavior based on a data distribution model[J]. Int J Electr Power Energy Syst 78:547–554

    Article  Google Scholar 

  7. Komazawa M, Itao K, Kobayashi H et al (2016) On human autonomic nervous activity related to behavior, daily and regional changes based on big data measurement via smartphone[J]. Health 08(9):827–845

    Article  Google Scholar 

  8. Amato F, Moscato V, Picariello A et al (2017) KIRA: A System for Knowledge-Based Access to Multimedia Art Collections[C]//. International Conference on Semantic Computing. IEEE Computer Society, IEEE: 338–343.

  9. Li C, Liu Y, Luo Y (2017) Multimedia cloud content distribution based on interest discovery and integrated utility of user[J]. Comput Ind Eng 109:1–14

    Article  Google Scholar 

  10. Liu P, Liu C (2017) Classification retrieval method for multimedia cloud resources based on Lagrange algorithm[J]. J Shenyang Univ Technol 39(4):433–437

    Google Scholar 

  11. Panchanathan S, Chakraborty S, Mcdaniel T et al (2016) Person-Centered Multimedia Computing: A New Paradigm Inspired by Assistive and Rehabilitative Applications[J]. IEEE Multimedia, 23(3):12–19

    Article  Google Scholar 

  12. Rawashdeh M, Alqurishi M, Alrakhami M et al (2017) A multimedia cloud-based framework for constant monitoring on obese patients[C]// IEEE international conference on multimedia & expo workshops. IEEE:139–144

  13. Robinson WN, Deng T, Developer Behavior QZ (2016) Sentiment from data mining open source repositories[C]// Hawaii international conference on system sciences. IEEE:3729–3738

  14. Smeulders A (2017) ACM SIGMM award for outstanding technical contributions to multimedia computing, communications and applications[C]// ACM on multimedia conference. ACM 818

  15. Tian Y, Chen M, Ubiquitous Multimedia SL (2016) Emerging research on multimedia computing[J]. IEEE Multimedia 23(2):12–15

    Article  Google Scholar 

  16. Zeinali B, Karsinos D, Moradi F. (2018) Progressive Scaled STT-RAM for Approximate Computing in Multimedia Applications[J]. IEEE Transactions on Circuits & Systems II Express Briefs, 65(7):938–942

  17. Zhang Y (2017) Optimization strategy of mobile data transmission based on optimal crowd feedback[J]. EURASIP J Embed Syst 2016(1):26

    Article  Google Scholar 

  18. Zhao L, Chen Z, Yang Y et al (2016) ncomplete Multi-View Clustering via Deep Semantic Mapping[J]. Neurocomputing, 275:1053–1062

Download references

Acknowledgments

This work is supported in part by National Natural Science Fund (61502204) and a project funded by theExcellent Specialties Program Development of Jiangsu Higher Education Institutions (PPZY2015C240).

Author information

Authors and Affiliations

  1. School of Internet of Things Technology, Wuxi Institute of Technology, Wuxi, Jiangsu, China

    Ying Xiao, Li Zhang & Ligong Hou

  2. Jiangnan University, Wuxi, Jiangsu, China

    Ying Xiao

Authors
  1. Ying Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ligong Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Xiao.

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

Xiao, Y., Zhang, L. & Hou, L. Autonomous multimedia cluster computing based on Cooperative Cognition data behavior measurement under multi cloud computing. Multimed Tools Appl 78, 8783–8797 (2019). https://doi.org/10.1007/s11042-018-6381-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6381-y

Keywords

Search

Navigation