Skip to main content

Maximize Profit for Big Data Processing in Distributed Datacenters

  • Chapter
  • First Online:
Resource Management for Big Data Platforms

Part of the book series: Computer Communications and Networks ((CCN))

  • 1515 Accesses

Abstract

The increasing demand of Big Data processing in distributed datacenters calls for a highly efficient framework to maximize profit of the cloud service providers, i.e., CSPs. In this work, we jointly consider the key parameters of datacenter operations to model service requests acceptance control, requests dispatching, and VM provisioning as an integrated optimization framework based on Lyapunov optimization theory. An efficient online algorithm is proposed to provide CSPs with the advices concerning the three important control decisions to obtain the maximal time-averaged profit over the long run. A rigorous mathematical analysis is given to verify that the proposed method is able to obtain a time averaged profit that is arbitrarily close to optimum, while keeping the system stable.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abbasi, Z., Pore, M., Gupta, S.K.S.: Online server and workload management for joint optimization of electricity cost and carbon footprint across data centers. In: 2014 IEEE International Parallel Distributed Processing Symposium, pp. 317–326 (2014)

    Google Scholar 

  2. Gao, P.X., Curtis, A.R., Wong, B., Keshav, S.: Its not easy being green. In: Proceedings of the ACM SIGCOMM 2012 Conference, pp. 211–222 (2012)

    Google Scholar 

  3. Georgiadis, L., Neely, M.J., Tassiulas, L.: Resource allocation and cross-layer control in wireless networks. Found. Trends Networking 1(1) (2006)

    Google Scholar 

  4. Gu, L., Zeng, D., Guo, S., Xiang, Y., Hu, J.: A general communication cost optimization framework for big data stream processing in geo-distributed data centers. IEEE Trans. Comput. Line (2015). doi:10.1109/TC.2015.2417566

    Google Scholar 

  5. Hines, M.R., Deshpande, U., Gopalan, K.: Post-copy live migration of virtual machines. Sigops Operating Syst. Rev. 43, 14–26 (2009)

    Article  Google Scholar 

  6. Liu, F., Zhou, Z., Jin, H., Li, B., Li, B., Jiang, H.: On arbitrating the power-performance tradeoff in saas clouds. IEEE Trans. Parallel Distrib. Syst. 25(10), 2648–2658 (2014)

    Article  Google Scholar 

  7. Liu, Z., Chen, Y., Bash, C., Wierman, A., Gmach, D., Wang, Z., Marwah, M., Hyser, C.: Renewable and cooling aware workload management for sustainable data centers. Perform. Eval. Rev. 40(1), 175–186 (2012)

    Article  Google Scholar 

  8. Neely, M.: Stochastic network optimization with application to communication and queueing systems. Synth. Lect. Commun. Netw. 3(1) (2010)

    Google Scholar 

  9. Valancius, V., Lumezanu, C., Feamster, N., Johari, R., Vazirani, V.V.: How many tiers? pricing in the internet transit market. In: Proceedings of the ACM SIGCOMM 2011 Conference, pp. 194–205 (2011)

    Google Scholar 

  10. Xu, H., Feng, C., Li, B.: Temperature aware workload management in geo-distributed datacenters. Acm Sigmetrics Perform. Eval. Rev. 41(1), 373–374 (2013)

    Article  MathSciNet  Google Scholar 

  11. Yao, Y., Huang, L., Sharma, A., Golubchik, L., Neely, M.: Data centers power reduction: A two time scale approach for delay tolerant workloads. In: 2012 Proceedings IEEE INFOCOM, pp. 1431–1439 (2012)

    Google Scholar 

  12. Zhang, Q., Zhu, Q., Zhani, M.F., Boutaba, R.: Dynamic service placement in geographically distributed clouds. In: 2012 IEEE International Conference on Distributed Computing Systems, pp. 526—535 (2012)

    Google Scholar 

  13. Zhao, J., Li, H., Wu, C., Li, Z., Zhang, Z., Lau, F.: Dynamic pricing and profit maximization for the cloud with geo-distributed data centers. In: 2014 Proceedings IEEE INFOCOM, pp. 118–126 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ji Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this chapter

Cite this chapter

Bao, W., Wang, J., Zhu, X. (2016). Maximize Profit for Big Data Processing in Distributed Datacenters. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44881-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44880-0

  • Online ISBN: 978-3-319-44881-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics