Skip to main content
SpringerLink
Log in

Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols

  • Chapter
  • First Online:
Intelligent Agents in Data-intensive Computing

Part of the book series: Studies in Big Data ((SBD,volume 14))

  • 1086 Accesses

Abstract

In the last years there has been considerable interest in using distributed systems, especially Cloud Systems, in any domains. Resource management contributes to ensure quality of services for any type of application, especially when a system involves elements of heterogeneity characterized by a variety of resources that may or may not be coupled with specific platforms or environments. A problem very close to the industry is the capability to allocate resources in an efficient way and estimate costs, especially when switching from one provider to another. In this chapter we present an extended work oriented on agreement–based resource allocation and a scheduling algorithm, aimed to bring an adaptive fault tolerant distributed system. For the agreement protocol we describe and analyze a 3-Tier structure of resources (hosts and virtual machines). Then an adaptive mechanism for agreement establishment is described. The allocation method considers workload distribution, resources heterogeneity, transparency, adaptability and also the ease to extend by combining with other scheduling algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Davies, K.: Best practices in big data storage. Tabor Communications Custom Publishing Group (2013). Accessed 20 May 2013

    Google Scholar 

  2. Sfrent, A., Pop, F.: Asymptotic scheduling for many task computing in big data platforms. Inf. Sci. 319, 71–91 (2015)

    Google Scholar 

  3. Gens, F.: IDC predictions 2013: competing on the 3rd platform. [Int. Data Corporation] (2012). Accessed 25 May 2013

    Google Scholar 

  4. Tutueanu, R.I., Pop, F., Vasile, M.A., Cristea, V.: Scheduling algorithm based on agreement protocol for cloud systems. In: Aversa, R., Koodziej, J., Zhang, J., Amato, F., Fortino, G. (eds.) Algorithms and Architectures for Parallel Processing. Lecture Notes in Computer Science, vol. 8286, pp. 94–101. Springer International Publishing (2013)

    Google Scholar 

  5. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  6. Mell, P., Grance, T.: The nist definition of cloud computing (draft). NIST special publication 800, 145 (2011)

    Google Scholar 

  7. Frincu, M.E., Craciun, C.: Multi–objective meta–heuristics for scheduling applications with high availability requirements and cost constraints in multi–cloud environments. In: Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing. UCC’11, pp. 267–274. IEEE Computer Society (2011)

    Google Scholar 

  8. Wang, L., Chen, D., Ranjan, R., Khan, S.U., Kołodziej, J., Wang, J.: Parallel processing of massive EEG data with mapreduce. In: Proceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems. ICPADS’12, pp. 164–171. IEEE Computer Society, Washington (2012)

    Google Scholar 

  9. Csaji, B.C., Monostori, L., Kfiadfiar, B.: Learning and cooperation in a distributed market-based production control system. In: Proceedings of the 5th International Workshop on Emergent Synthesis, pp. 109–116. Citeseer (2004)

    Google Scholar 

  10. Iqbal, W., Dailey, M., Carrera, D.: SLA–driven adaptive resource management for web applications on a heterogeneous compute cloud. In: Cloud Computing, pp. 243–253. Springer (2009)

    Google Scholar 

  11. Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gen. Comput. Syst. 27(6), 871–879 (2011)

    Article  Google Scholar 

  12. Beloglazov, A., Abawajy, J., Buyya, R.: Energy–aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems 28(5), 755–768 (2012). Special Section: energy efficiency in large-scale distributed systems

    Google Scholar 

  13. Ion, M., Pop, F., Dobre, C., Cristea, V.: Dynamic resources allocation in grid environments. In: Proceedings of the 2009 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. SYNASC’09, pp. 213–220. IEEE Computer Society, Washington (2009)

    Google Scholar 

  14. Moise, D., Moise, E., Pop, F., Cristea, V.: Resource coallocation for scheduling tasks with dependencies, in grid. In: Procedins of the Second International Workshop on High Performance in Grid Middleware (HiPerGRID), pp. 41–48. IEEE Romania, Bucharest (2008). ISSN: 2065–0701

    Google Scholar 

  15. Moise, I., Moise, D., Pop, F., Cristea, V.: Advance reservation of resources for task execution in grid environments. In: Proceedins of the Second International Workshop on High Performance in Grid Middleware (HiPerGRID), pp. 57–64. IEEE Romania, Bucharest (2008). Adaptive Res. Alloc. in Cloud Comp. based on Agreement Protocols 21. ISSN: 2065–0701

    Google Scholar 

  16. Vizan, S., Stefanescu, R., Pop, F., Cristea, V.: Decentralized meta–scheduler for economy grid environments. In: Proceedings of the Second International Workshop on High Performance in Grid Middleware (HiPerGRID), pp. 111–122. IEEE Romania, Bucharest (2008). ISSN: 2065–0701

    Google Scholar 

  17. Hagras, T., Janeek, J.: Static versus dinamic list–scheduling performance comparison. Acta Polytech. 43(6) (2003)

    Google Scholar 

  18. Kaur, K., Chhabra, A., Singh, G.: Heuristics based genetic algorithm for scheduling static tasks in homogeneous parallel system. Int. J. Comput. Sci. Secur. 4(2), 183–198 (2010)

    Google Scholar 

  19. Kołodziej, J., Khan, S.U.: Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment. Inf. Sci. 214, 1–19 (2012)

    Article  Google Scholar 

  20. Li, B., Song, A.M., Song, J.: A distributed qos–constraint task scheduling scheme in cloud computing environment: model and algorithm. AISS: Adv. Inf. Sci. Serv. Sci. 4(5), 283–291 (2012)

    Article  Google Scholar 

  21. Iordache, G., Boboila, S., Pop, F., Stratan, C., Cristea, V.: A decentralized strategy for genetic scheduling in heterogeneous environments. Int. J. Multi-agent Grid Syst. 3(4) (2007). ISSN: 1574–1702

    Google Scholar 

  22. Iordache, G., Boboila, S., Pop, F., Stratan, C., Cristea, V.: A decentralized strategy for genetic scheduling in heterogeneous environments. In: Proceedings of on the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE, vol. 4276, pp. 1234–1251. Springer, Montpellier Oct 29–Nov 3 (2006). ISBN: 978–3–540–48274–1

    Google Scholar 

  23. Iordache, G., Boboila, S., Pop, F., Stratan, C., Cristea, V.: Decentralized Grid Scheduling Using Genetic Algorithms, pp. 215–246. Springer (2008)

    Google Scholar 

  24. Jang, S.H., Kim, T.Y., Kim, J.K., Lee, J.S.: The study of genetic algorithm- based task scheduling for cloud computing. Int. J. Control Autom. 5(4), 157–162 (2012)

    Google Scholar 

  25. Xu, C.Z., Rao, J., Bu, X.: Url: a unied reinforcement learning approach for autonomic cloud management. J. Parallel Distrib. Comput. 72(2), 95–105 (2012)

    Article  Google Scholar 

  26. Frincu, M.E., Villegas, N.M., Petcu, D., Muller, H.A., Rouvoy, R.: Self–healing distributed scheduling platform. In: Procedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. CCGRID’11, pp. 225–234. IEEE Computer Society, Washington (2011)

    Google Scholar 

  27. Wang, L., Khan, S.U., Chen, D., Kołodziej, J., Ranjan, R., Xu, C.Z., Zomaya, A.: Energy-ware parallel task scheduling in a cluster. Future Gener. Comput. Syst. 29(7), 1661–1670 (2013)

    Article  Google Scholar 

  28. Naik, P., Agrawal, S., Murthy, S.: A survey on various task scheduling algorithms toward load balancing in public cloud. Am. J. Appl. Math. 3(1–2), 14–17 (2015)

    Google Scholar 

  29. Goyal, T., Singh, A., Agrawal, A.: Cloudsim: simulator for cloud computing in-frastructure and modeling. Procedia Eng. 38, 3566–3572 (2012)

    Article  Google Scholar 

  30. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Soft.: Pract. Exp. 41(1), 23–50 (2011)

    Google Scholar 

  31. Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: An approach to universal topology generation. In: Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems. MASCOTS’01, pp. 346–352. IEEE Computer Society, Washington (2001)

    Google Scholar 

Download references

Acknowledgments

The research presented in this paper is supported by projects: CyberWater grant of the Romanian National Authority for Scientific Research, CNDI-UEFISCDI, project number 47/2012; MobiWay: Mobility Beyond Individualism: an Integrated Platform for Intelligent Transportation Systems of Tomorrow-PN-II-PT-PCCA-2013-4-0321; clueFarm: Information system based on cloud services accessible through mobile devices, to increase product quality and business development farms-PN-II-PT-PCCA-2013-4-0870. We would like to thank the reviewers for their time and expertise, constructive comments and valuable insight.

Author information

Authors and Affiliations

  1. Faculty of Automatic Control and Computers, Computer Science Department, University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop, Radu-Ioan Tutueanu, Ciprian Barbieru & Mihaela-Andreea Vasile

  2. Cracow University of Technology, Krakow, Poland

    Joanna Kołodziej

Authors
  1. Florin Pop
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radu-Ioan Tutueanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ciprian Barbieru
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mihaela-Andreea Vasile
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joanna Kołodziej
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Florin Pop .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  2. Faculty of Sciences, University of Lisbon, Lisboa, Portugal

    Luís Correia

  3. Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain

    José Manuel Molina

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Pop, F., Tutueanu, RI., Barbieru, C., Vasile, MA., Kołodziej, J. (2016). Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols. In: Kołodziej, J., Correia, L., Manuel Molina, J. (eds) Intelligent Agents in Data-intensive Computing. Studies in Big Data, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-23742-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23742-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23741-1

  • Online ISBN: 978-3-319-23742-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation