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Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources

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Abstract

Fair and efficient allocation of multiple types of resources is a fundamental goal in cloud computing systems. Due to the heterogeneous resource requirements of a user’s jobs on CPU, memory, etc., fairness of allocation is difficult to ensure while maximizing efficiency. Existing representative multi-resource fair allocation mechanisms, such as dominant resource fairness and its follow-up work, can hardly achieve maximum efficiency while ensuring fairness. To overcome this drawback, we propose a new fair allocation mechanism, called generalized asset fairness (GAF), to maximize the system resource utilization and ensure fairness. We show that GAF satisfies many desired fairness properties. To implement our resource allocation mechanism, we design a scheduling algorithm to find feasible solutions. The experimental results show that GAF can produce a fair allocation with higher resource utilization than all previous known fair mechanisms; GAF also performs well in resource sharing.

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Data availability statement

The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.

References

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

  2. 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)

  3. 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)

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

    Article  Google Scholar 

  5. Parkes, D.C., Procaccia, A.D., Shah, N.: Beyond dominant resource fairness: extensions, limitations, and indivisibilities. ACM Trans. Econ. Comput. 3(1), 1–22 (2015)

    Article  MathSciNet  Google Scholar 

  6. Kash, I., Procaccia, A.D., Shah, N.: No agent left behind: dynamic fair division of multiple resources. J. Artif. Intell. Res. 51(2), 579–603 (2014)

    Article  MathSciNet  Google Scholar 

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

  8. 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. 11(4), 245–257 (2015)

    Article  Google Scholar 

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

  10. Tang, S., Niu, Z., He, B., Lee, B.S., Yu, C.: Long-term multi-resource fairness for pay-as-you use computing systems. IEEE Trans. Parallel Distrib. Syst. 29(5), 1147–1160 (2018)

    Article  Google Scholar 

  11. 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 

  12. Liu, X., Zhang, X., Li, W., Zhang, X.: Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems. Computing 99(12), 1231–1255 (2017)

    Article  MathSciNet  Google Scholar 

  13. Khamse-Ashari, J., Lambadaris I., Kesidis G., Urgaonkar, B., Zhao, Y.: Per-server dominant-share fairness (PS-DSF): a multi-resource fair allocation mechanism for heterogeneous servers. In: 2017 IEEE International Conference on Communications, pp. 1–7 (2017)

  14. Joe-Wong, C., 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 

  15. Jiang, S., Wu, J.: 2-dominant resource fairness: fairness-efficiency tradeoffs in multi-resource allocation. In: 37th International Performance Computing and Communications Conference, pp. 1–8 (2018)

  16. Khamse-Ashari, J., Lambadaris, I., Kesidis, G., Urgaonkar, B., Zhao, Y.: An efficient and fair multi-resource allocation mechanism for heterogeneous servers. IEEE Trans. Parallel Distrib. Syst. 29(12), 2686–2699 (2018)

    Article  Google Scholar 

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

  18. Lu, Q., Yao, J., Qi, Z., He, B., Guan, H.: Fairness-efficiency allocation of CPU-GPU heterogeneous resources. IEEE Trans. Serv. Comput. 12(3), 474–488 (2019)

    Article  Google Scholar 

  19. Wang H., Varman P.: Balancing fairness and efficiency in tiered storage systems with Bottleneck-Aware allocation. In: Proceedings of the USENIX Conference on File and Storage Technologies, pp. 229–242 (2014)

  20. Liu, J., Zhu, C.: No user left behind: dynamic bottleneck-aware allocation of multiple resources. Cluster Comput. 22(4), 10219–10227 (2019)

    Article  Google Scholar 

  21. Tang, S., Yu, C., Li, Y.: Fairness-efficiency scheduling for cloud computing with soft fairness guarantees. IEEE Trans. Cloud Comput. (2020). https://doi.org/10.1109/tcc.2020.3021084

  22. Kurokawa, D., Procaccia, A.D., Shah, N.: Leximin allocations in the real world. ACM Trans. Econ. Comput. 6(3–4), 1–24 (2018)

    Article  MathSciNet  Google Scholar 

  23. Alibaba cluster trace (2018). https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018

  24. Google Trace (2011). https://github.com/google/cluster-data/blob/master/ClusterData2011

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China [Nos. 12071417, 61762091], the Program for Excellent Young Talents of Yunnan University, the Training Program of the National Science Fund for Distinguished Young Scholars, IRTSTYN, and the Scientific Research Foundation of Yunnan Provincial Department of Education [No. 2019J0826].

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650504, People’s Republic of China

    Xuejie Zhang, Jie Li & Guibing Li

  2. School of Mathematics and Statistics, Yunnan University, Kunming, 650504, People’s Republic of China

    Weidong Li

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  1. Xuejie Zhang
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  2. Jie Li
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Correspondence to Weidong Li.

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Zhang, X., Li, J., Li, G. et al. Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources. Cluster Comput 25, 3389–3403 (2022). https://doi.org/10.1007/s10586-022-03548-9

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  • DOI: https://doi.org/10.1007/s10586-022-03548-9

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