Skip to main content
Springer Nature Link
Log in

Multiresource fair allocation with time window constraints

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

Abstract

We study the problem of multiresource fair allocation with time window constraints for user tasks in cloud computing systems where users can enter and leave the system multiple times. We propose a new mechanism, dominant resource fairness with time window constraints (DRFTW), that extends the concepts of dominant resource fairness and lexicographically max-min fairness to the case where user tasks have time window constraints. We design an algorithm for DRFTW to produce the optimal allocations. DRFTW has several highly desirable properties: no user can improve its performance without making at least one other user worse off; no user prefers even allocations; no user envies other users’ allocations; and no user can increase its utility by providing false information. Simulations driven by Alibaba Cluster Trace further revealed that DRFTW can significantly increase the minimum dominant share and improve fairness among users compared with three conventional fair allocation methods.

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
$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

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
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets generated or analyzed during this study are available online at https://github.com/alibaba/clusterdata/blob/v2018/cluster-trace-v2018/trace_2018.md.

References

  1. Ghodsi A, Zaharia M, Hindman B, Konwinski A, Shenker S, Stoica I (2011) Dominant resource fairness: fair allocation of multiple resource types. In: Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation, pp 323–336

  2. Radunovic B, Boudec JL (2007) A unified framework for max-min and min-max fairness with applications. IEEE/ACM Trans Netw 15(05):1073–1083

    Article  Google Scholar 

  3. Chakraborty M, Igarashi A, Suksompong W, Zick Y (2021) Weighted envy-freeness in indivisible item allocation. ACM Trans Econ Comput TEAC 9(3):1–39

    Article  MathSciNet  Google Scholar 

  4. Parikh SM (2013) A survey on cloud computing resource allocation techniques. In: 2013 Nirma University International Conference on Engineering (NUiCONE), pp 1–5

  5. Li J, Zhang J, Li W, Zhang X (2019) A fair distribution strategy based on shared fair and time-varying resource demand. J Comput Res Dev 56(7):1534

    Google Scholar 

  6. Poullie P, Bocek T, Stiller B (2018) A survey of the state-of-the-art in fair multi-resource allocations for data centers. IEEE Trans Netw Serv Manage 15(1):169–183

    Article  Google Scholar 

  7. Jaffe J (1981) Bottleneck flow control. IEEE Trans Commun 29(7):954–962

    Article  MathSciNet  Google Scholar 

  8. Sadok H, Campista MEM, Costa LHMK (2021) Stateful DRF: considering the past in a multi-resource allocation. IEEE Trans Comput 70(7):1094–1105

    Article  MathSciNet  MATH  Google Scholar 

  9. Floyd S (2008) Metrics for the evaluation of congestion control mechanisms. RFC 5166, Citeseer

  10. Kelly F, Maulloo A, Tan D (1998) Rate control for communication networks: shadow prices, proportional fairness and stability. J Oper Res Soc 3:49

    MATH  Google Scholar 

  11. Megiddo N (1974) Optimal flows in networks with multiple sources and sinks. Math Program 7(1):97–107

    Article  MathSciNet  MATH  Google Scholar 

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

  13. Zhang X, Liu X, Li W, Zhang X (2016) Dynamic fair allocation of multi-resources based on shared resource quantity. J Commun 37(7):151

    Google Scholar 

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

  15. Zahedi SM, Lee BC (2014) Resource elasticity fairness with sharing incentives for multiprocessors. In: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems

  16. Li W, Liu X, Zhang X, Zhang X (2017) A further analysis of the dynamic dominant resource fairness mechanism. In: International Workshop on Frontiers in Algorithmics, pp 163–174

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Tang S, Yu C, Li Y (2022) Fairness-efficiency scheduling for cloud computing with soft fairness guarantees. IEEE Trans Cloud Comput 10(3):1806–1818

    Article  Google Scholar 

  20. Li X, Li W, Zhang X (2022) Extended efficiency and soft-fairness multiresource allocation in a cloud computing system. Computing, 1–29

  21. Vardi S, Psomas A, Friedman E (2022) Dynamic fair resource division. Math Oper Res 47(2):945–968

    Article  MathSciNet  MATH  Google Scholar 

  22. Kash I, Procaccia AD, Shah N (2014) No agent left behind: dynamic fair division of multiple resources. J Artif Int Res 51(1):579–603

    MathSciNet  MATH  Google Scholar 

  23. Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA (2012) Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing, pp 1–13

  24. Friedman E, Psomas CA, Vardi S (2017) Controlled dynamic fair division. In: Proceedings of the 2017 ACM Conference on Economics and Computation, pp 461–478

  25. Alibaba Data Trace [EB/OL]. https://github.com/alibaba/clusterdata/blob/v2018/cluster-trace-v2018/trace_2018.md

  26. Kelly FP, Maulloo AK, Tan DKH (1998) Rate control for communication networks: shadow prices, proportional fairness and stability. J Oper Res Soc 49(3):237–252

    Article  MATH  Google Scholar 

  27. Ghodsi A, Zaharia M, Shenker S, Stoica I (2013) Choosy: Max-min fair sharing for datacenter jobs with constraints. In: Proceedings of the 8th ACM European Conference on Computer Systems, pp 365–378

  28. Parkes DC, Procaccia AD, Shah N (2015) Beyond dominant resource fairness: extensions, limitations, and indivisibilities. ACM Trans Econ Comput TEAC 3(1):1–22

    Article  MathSciNet  Google Scholar 

  29. Vavilapalli V.K, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S, Saha B, Curino C, O’Malley O, Radia S, Reed B, Baldeschwieler E (2013) Apache hadoop yarn: Yet another resource negotiator. In: Proceedings of the 4th Annual Symposium on Cloud Computing, pp 1–16

  30. Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph A.D, Katz R, Shenker S, Stoica I (2011) Mesos: a platform for fine-grained resource sharing in the data center. In: 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI 11), pp 295–308

  31. Meskar E, Liang B (2018) Fair multi-resource allocation with external resource for mobile edge computing. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp 184–189

  32. Li W, Liu X, Zhang X, Zhang X (2017) Multi-resource fair allocation with bounded number of tasks in cloud computing systems. In: National Conference of Theoretical Computer Science, pp 3–17

  33. Zhang X, Li J, Li G, Li W (2022) Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources. Clus Comput 5:3389–403

    Article  Google Scholar 

  34. Waldspurger CA (1996) Lottery and stride scheduling: flexibile proportional-share resource management. PhD thesis, PhD thesis, MIT, Laboratory of Computer Science, USA

  35. Li X, Li W, Zhang X (2023) Multi-resource fair allocation with bandwidth requirement compression in the cloud-edge system. Comput Electr Eng 105:108510

    Article  Google Scholar 

Download references

Acknowledgements

We thank the associate editor and the reviewers for their useful feedback that improved this paper.

Funding

This work was supported in part by the National Natural Science Foundation of China [Nos. 12071417, 62266051 and 62062065] the 14th Postgraduate Innovation Project of Yunnan University [No. KC-22223141].

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650500, People’s Republic of China

    Xingxing Li & Xuejie Zhang

  2. School of Mathematics and Statistics, Yunnan University, Kunming, 650500, People’s Republic of China

    Weidong Li

Authors
  1. Xingxing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weidong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuejie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XL performed the data analysis and wrote the manuscript. WL contributed to the analysis and manuscript preparation. XZ helped perform the analysis with constructive discussion. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xuejie Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Li, W. & Zhang, X. Multiresource fair allocation with time window constraints. J Supercomput 79, 15927–15954 (2023). https://doi.org/10.1007/s11227-023-05248-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05248-6

Keywords

Search

Navigation