User-oriented cloud resource scheduling with feedback integration | The Journal of Supercomputing Skip to main content
Springer Nature Link
Log in

User-oriented cloud resource scheduling with feedback integration

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

Abstract

Resource scheduling has been one of the key challenges facing both academia and industry ever since the inauguration of cloud computing. Most of the existing research and practices have been focused on the maximization of the profits of cloud providers, whereas attention to the real needs of cloud users has largely been neglected. In this research, we propose a resource scheduling mechanism empowered with a relevance feedback network, which can be employed by a cloud provider to better meet a user’s resource needs. Our approach is a continuous refinement process that involves three stages: resource matching, resource selection, and feedback integration, where the feedback integration stage allows the resource scheduling history of a user to be considered to update the user’s resource demands and preference. The feedback information integrated in one cycle will effectively adjust the resource matching and selection in the next cycle. Incrementally, this mechanism will produce resource selections that are closer and closer to the user’s real needs. Simulation results indicated that this relevance feedback scheduling mechanism is very effective in satisfying users’ diverse requirements, and it also performs well in terms of the resource utilization rate from the cloud provider’s perspective.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /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 (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Foster I, Zhao Y, Raicu I, Lu SY (2008) Cloud computing and grid computing 360-degree compared. In: Proceedings of the Grid Computing Environments Workshop (GCE’08). New York: IEEE Press, pp 1–10

  2. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A et al (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  3. Mell P, Grance T (2009) The NIST Definition of Cloud Computing, Version 15. White Paper. V15

  4. Google App Engine[EB/OL] (2008) http://appengine.google.com

  5. Amazon Elastic Compute Cloud (EC2) [EB/OL] (2008) http://www.amazon.com/ec2/

  6. Sims K (2009) IBM introduces ready-to-use cloud computing collaboration services get clients started with cloud computing [EB/OL]. http://www-03.ibm.com/press/us/en/pressrelease/22613.wss

  7. Microsoft Azure [EB/OL] (2008) http://www.microsoft.com/azure/

  8. Asvanund A, Krishnan R, Smith M, Telang R (2003) Interest-based self-organizing peer-to-peer networks: a club economics approach. In: Proceedings of the 13th Workshop on Information Technology and Systems

  9. Crespo A, Garcia-Molina H (2005) Semantic overlay networks for P2P system. In: Proceedings of the 3rd Int’l Workshop on Agents and Peer-to-Peer Computing. Lecture Notes in Computer Science Volume 3601. pp 1–13

  10. Smith S, Albaum G (2004) Fundamentals of marketing research. SAGE Publications, Thousand oaks

    Google Scholar 

  11. Rocchio JJ (1971) Relevance feedback in information retrieval. In: The smart retrieval system—Experiments in automatic document processing. Prentice Hall, Englewood Cliffs, pp 313–323

  12. Eucalyptus [EB/OL] (2008) https://www.eucalyptus.com

  13. OpenNebula Project[EB/OL (2008) http://www.opennebula.org

  14. Nimbus [EB/OL] (2008) http://www.nimbusproject.org

  15. Maheswaran M, Ali S, Siegel H, Hensgen D, Freund R (1999) Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J Parall Distrib Comp 59(2):30–44

    Google Scholar 

  16. Gu JH, Hu JH, Zhao TH, Sun GF (2012) A new resource scheduling strategy based on genetic algorithm in cloud computing environment. J Comp 7(1):42–52

    Google Scholar 

  17. Pandit D, Chattopadhyay S, Chattopadhyay M, Chaki N (2014) Resource allocation in cloud using simulated annealing. In: Proceedings of 2014 International Conference on Applications and Innovations in Mobile Computing (AIMoC’14). pp 21–27

  18. Lu X, Gu ZL (2011) A load-adaptive cloud resource scheduling model based on ant colony algorithm. In: Proceedings of the 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS’11). pp 296–300

  19. Buyya R, Yeo CS, Venugopal S (2008) Market-oriented cloud computing: vision, hype, and reality for delivering IT services as computing utilities. In Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC’08). pp 5–13

  20. Salehi MA, Buyya R (2010) Adapting market-oriented scheduling policies for cloud computing. Algorithms and Architectures for Parallel Processing. Lecture Notes in Computer Science Volume 6081. pp 351–362

  21. Ding D, Luo SW, Ai LH (2012) An adaptive double auction mechanism for cloud resource allocation. J Commun 33(Z1):132–140 in chinese

    Google Scholar 

  22. Grover J, Katiyar S (2013) Agent based dynamic load balancing in Cloud Computing. In: Proceedings of 2013 International Conference on Human Computer Interactions (ICHCI). pp 1–6

  23. Gupta S, Mittal A, Sharma A (2013) Multi-cloud computing environment with an agent based efficient scheduling method. In: Proceedings of the Second International Conference on Advances in Information Technology (AIT ’13). pp 92–95

  24. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    MathSciNet  Google Scholar 

  25. Himani, Singh Sidhu H (2014) Comparative analysis of scheduling algorithms of Cloudsim in cloud computing. Int J Comp Appl 97(16):29–33

    Google Scholar 

  26. Ding D, Luo SW, Ai LH, Li YD (2012) A user preference driven approach for multi-QoS constrained task scheduling in grid. In: Proceedings of the 13th International Conference on Parallel and Distributed Computing, Application, and Technologies(PDCAT’12). Beijing, China: IEEE Computer Society. pp 493–498

  27. Liu P (2012) Research of cloud resource allocation based on continuous double auction. East China Jiaotong University, Nanchang

    Google Scholar 

Download references

Acknowledgments

This work is supported by National Nature Science Foundation of China (61300176), Fundamental Research Funds for the Central Universities (2013JBM019), Special Fund for Fast Sharing of Science Paper in Net Era by CSTD (2013113), Beijing Higher Education Young Elite Teacher Project (YETP0546) and China Scholarship Council (201307095016).

Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Ding Ding & Siwei Luo

  2. School of Engineering, Behrend College, The Pennsylvania State University, Erie, PA, 16563, USA

    Xiaocong Fan

Authors
  1. Ding Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaocong Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siwei Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ding Ding.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, D., Fan, X. & Luo, S. User-oriented cloud resource scheduling with feedback integration. J Supercomput 72, 3114–3135 (2016). https://doi.org/10.1007/s11227-015-1530-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-015-1530-9

Keywords

Search

Navigation