Skip to main content

A Further Analysis of the Dynamic Dominant Resource Fairness Mechanism

  • Conference paper
  • First Online:
Frontiers in Algorithmics (FAW 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10336))

Included in the following conference series:

Abstract

Multi-resource fair allocation has been a hot topic in cloud computing. Recently, a dynamic dominant resource fairness mechanism (DDRF) is proposed for dynamic multi-resource fair allocation. In this paper, we develop a linear-time algorithm to find a DDRF solution at each step. Moreover, we give the competitive ratios of the DDRF mechanism under three widely used objectives.

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
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Annamalai, C., Kalaitzis, C., Svensson, O.: Combinatorial algorithm for restricted max-min fair allocation. In: Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1357–1372 (2015)

    Google Scholar 

  2. Asadpour, A., Saberi, A.: An approximation algorithm for max-min fair allocation of indivisible goods. SIAM J. Comput. 39(7), 2970–2989 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  3. Aumann, Y., Dombb, Y.: The efficiency of fair division with connected pieces. ACM Trans. Econ. Comput. 3(4) (2015). Article No. 23

    Google Scholar 

  4. Bertsimas, D., Farias, V.F., Trichakis, N.: The price of fairness. Oper. Res. 59(1), 17–31 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bhattacharya, A.A., Culler, D., Friedman, E., Ghodsi, A., Shenker, S., Stoica, I.: Hierarchical scheduling for diverse datacenter workloads. In: Proceedings of the 4th Annual Symposium on Cloud Computing, SOCC 2013 (2013). Article No. 4

    Google Scholar 

  6. Bonald, T., Roberts, J.: Enhanced cluster computing performance through proportional fairness. Perform. Eval. 79, 134–145 (2014)

    Article  Google Scholar 

  7. Bonald, T., Roberts, J.: Multi-resource fairness: objectives, algorithms and performance. ACM SIGMETRICS Perform. Eval. Rev. 43(1), 31–42 (2015)

    Article  Google Scholar 

  8. Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University, Cambridge (1998)

    MATH  Google Scholar 

  9. Caragiannis, I., Kaklamanis, C., Kanellopoulos, P., Kyropoulou, M.: The efficiency of fair division. Theory Comput. Syst. 50(4), 589–610 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chowdhury, M., Liu, Z., Ghodsi, A., Stoica, I.: HUG: multi-resource fairness for correlated and elastic demands. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2016), pp. 407–424 (2016)

    Google Scholar 

  11. Dolev, D., Feitelson, D.G., Halpern, J.Y., Kupferman, R., Linial, N.: No justified complaints: on fair sharing of multiple resources. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 68–75 (2012)

    Google Scholar 

  12. Friedman, E., Ghodsi, A., Psomas, C.-A.: Strategyproof allocation of discrete jobs on multiple machines. In: Proceedings of the Fifteenth ACM Conference on Economics and Computation, pp. 529–546 (2014)

    Google Scholar 

  13. Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S., Stoica, I.: Dominant resource fairness: fair allocation of multiple resource types. In: Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation, pp. 24–37 (2011)

    Google Scholar 

  14. Gutman, A., Nisan, N.: Fair allocation without trade. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, pp. 719–728 (2012)

    Google Scholar 

  15. Jin, Y., Hayashi, M.: Efficiency comparison between proportional fairness and dominant resource fairness with two different type resources. In: 2016 Annual Conference on Information Science and Systems (CISS), pp. 643–648 (2016)

    Google Scholar 

  16. Kash, I., Procaccia, A.D., Shah, N.: No agent left behind: dynamic fair division of multiple resources. J. Artif. Intell. Res. 51, 579–603 (2014)

    MathSciNet  MATH  Google Scholar 

  17. Li, W., Liu, X., Zhang, X., Zhang, X.: Dynamic fair allocation of multiple resources with bounded number of tasks in cloud computing systems. Multiagent Grid Syst. Int. J. 11, 245–257 (2015)

    Article  Google Scholar 

  18. Parkes, D.C., Procaccia, A.D., Shah, N.: Beyond dominant resource fairness: extensions, limitations, and indivisibilities. ACM Trans. Econ. Comput. 3(1) (2015). Article No. 3

    Google Scholar 

  19. Procaccia, A.D.: Cake cutting: not just child’s play. Commun. ACM 56(7), 78–87 (2013)

    Article  Google Scholar 

  20. Psomas, C.-A., Schwartz, J.: Beyond beyond dominant resource fairness: indivisible resource allocation in clusters. Technical report Berkeley (2013)

    Google Scholar 

  21. Wang, H., Varman, P.J.: Balancing fairness and efficiency in tiered storage systems with bottleneck-aware allocation. In: Proceedings of the 12th USENIX Conference on File and Storage Technologies, pp. 229–242 (2014)

    Google Scholar 

  22. Wang, W., Liang, B., Li, B.: Multi-resource fair allocation in heterogeneous cloud computing systems. IEEE Trans. Parallel Distrib. Syst. 26(10), 2822–2835 (2015)

    Article  Google Scholar 

  23. Wang, W., Li, B., Liang, B., Li, J.: Towards multi-resource fair allocation with placement constraints. In: Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science, pp. 415–416 (2016)

    Google Scholar 

  24. Wong, C.J., Sen, S., Lan, T., Chiang, M.: Multi-resource allocation: fairness efficiency tradeoffs in a unifying framework. IEEE/ACM Trans. Netw. 21(6), 1785–1798 (2013)

    Article  Google Scholar 

  25. Zahedi, S.M., Lee, B.C.: REF: resource elasticity fairness with sharing incentives for multiprocessors. ACM SIGARCH Comput. Architect. News 42(1), 145–160 (2014)

    Google Scholar 

  26. Zarchy, D., Hay, D., Schapira, M.: Capturing resource tradeoffs in fair multi-resource allocation. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 1062–1070 (2015)

    Google Scholar 

Download references

Acknowledgment

The work is supported in part by the National Natural Science Foundation of China [Nos. 61662088, 11301466], the Natural Science Foundation of Yunnan Province of China [No. 2014FB114], and IRTSTYN.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuejie Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Li, W., Liu, X., Zhang, X., Zhang, X. (2017). A Further Analysis of the Dynamic Dominant Resource Fairness Mechanism. In: Xiao, M., Rosamond, F. (eds) Frontiers in Algorithmics. FAW 2017. Lecture Notes in Computer Science(), vol 10336. Springer, Cham. https://doi.org/10.1007/978-3-319-59605-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59605-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59604-4

  • Online ISBN: 978-3-319-59605-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics