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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Asadpour, A., Saberi, A.: An approximation algorithm for max-min fair allocation of indivisible goods. SIAM J. Comput. 39(7), 2970–2989 (2012)
Aumann, Y., Dombb, Y.: The efficiency of fair division with connected pieces. ACM Trans. Econ. Comput. 3(4) (2015). Article No. 23
Bertsimas, D., Farias, V.F., Trichakis, N.: The price of fairness. Oper. Res. 59(1), 17–31 (2011)
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
Bonald, T., Roberts, J.: Enhanced cluster computing performance through proportional fairness. Perform. Eval. 79, 134–145 (2014)
Bonald, T., Roberts, J.: Multi-resource fairness: objectives, algorithms and performance. ACM SIGMETRICS Perform. Eval. Rev. 43(1), 31–42 (2015)
Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University, Cambridge (1998)
Caragiannis, I., Kaklamanis, C., Kanellopoulos, P., Kyropoulou, M.: The efficiency of fair division. Theory Comput. Syst. 50(4), 589–610 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
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
Procaccia, A.D.: Cake cutting: not just child’s play. Commun. ACM 56(7), 78–87 (2013)
Psomas, C.-A., Schwartz, J.: Beyond beyond dominant resource fairness: indivisible resource allocation in clusters. Technical report Berkeley (2013)
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)
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)
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)
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)
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)
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)
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)