Abstract
Efficiency and fairness are two essential objectives for multiresource allocations in shared cloud computing systems. Due to the different demands of different users and the different capacities of each resource, it is impossible for multiresource allocations to achieve absolute fairness and maximum efficiency simultaneously. In this paper, we generalize dominant resource fairness (DRF) and propose a new allocation mechanism, max–min efficiency DRF (MME-DRF), to achieve a tradeoff between fairness and efficiency. MME-DRF first fairly allocates some resources to ensure a lower bound of relative soft fairness among users. Then, MME-DRF allocates the remaining resources with the goal of maximizing the minimum resource utilization. MME-DRF can obtain a max–min resource utilization that directly reflects the overall resource utilization of the system. Rigorous proofs show that MME-DRF satisfies four desirable properties, e.g., the sharing incentive, soft fairness, Pareto efficiency and weighted envy freeness. In addition, we develop an algorithm for MME-DRF and evaluate it via simulations driven by examples and Google cluster traces. The simulation results show that MME-DRF guarantees soft fairness and significantly improves the resource utilization of the system.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Ealiyas A, Jeno Lovesum SP (2018) Resource allocation and scheduling methods in cloud - a survey. In: 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), pp 601–604
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 Network Serv Manag 15(1):169–183
Parikh SM (2013) A survey on cloud computing resource allocation techniques. In: 2013 Nirma University International Conference on Engineering (NUiCONE), pp 1–5
Vavilapalli VK, Murthy AC, Douglas C, et al (2013) Apache hadoop yarn: Yet another resource negotiator. In: Proceedings of the 4th annual Symposium on Cloud Computing, pp 1–16
Hindman B, Konwinski A, Zaharia M, et al (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
Ghodsi A, Zaharia M, Hindman B, et al (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
Li J, Zhang J, Li W et al (2019) A fair distribution strategy based on shared fair and time-varying resource demand. J Comput Res Develop 56(7):1534–1544
Gutman A, Nisan N (2012) Fair allocation without trade. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2, AAMAS ’12, pp 719–728
Zhang X, Liu X, Li W et al (2016) Dynamic fair allocation of multi-resources based on shared resource quantity. J Commun 37(7):151–160
Rényi A (1961) On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, pp 547–561
Jain RK, Chiu DW, Hawe WR et al (1984) A quantitative measure of fairness and discrimination. Digital Equipment Corporation, Hudson, MA, Eastern Research Laboratory
Zukerman M, Tan L, Wang H et al (2005) Efficiency-fairness tradeoff in telecommunications networks. IEEE Commun Lett 9(7):643–645
Joe-Wong C, Sen S, Lan T et al (2013) Multiresource allocation: fairness-efficiency tradeoffs in a unifying framework. IEEE/ACM Trans Network 21(6):1785–1798
Tang S, Yu C, Li Y (2020) Fairness-efficiency scheduling for cloud computing with soft fairness guarantees. IEEE Trans Cloud Comput 2:1–10
Bonald T, Roberts J (2014) Enhanced cluster computing performance through proportional fairness. Perfor Evaluat 79:134–145
Khamse-Ashari J, Lambadaris I, Kesidis G et al (2018) An efficient and fair multi-resource allocation mechanism for heterogeneous servers. IEEE Trans Parall Distrib Syst 29(12):2686–2699
Wang W, Feng C, Li B, et al (2014) On the fairness-efficiency tradeoff for packet processing with multiple resources. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, CoNEXT ’14. pp 235–248
Danna E, Mandal S, Singh A (2012) A practical algorithm for balancing the max-min fairness and throughput objectives in traffic engineering. In: 2012 Proceedings IEEE INFOCOM, pp 846–854
Jiang S, Wu J (2021) Multi-resource allocation in cloud data centers: A trade-off on fairness and efficiency. Concurr Comput Pract Exper 33(6):e6061
Wilkes J (2011) More google cluster data. Available online: http://googleresearch.blogspot.com/2011/11/more-googlecluster-data.html (accessed on 21 October 2022), google research blog, 2011
Li W, Liu X, Zhang X et al (2017) Multi-resource fair allocation with bounded number of tasks in cloud computing systems. Natl Confer Theoret Comput Sci 20:3–17
Liu X, Zhang X, Li W et al (2017) Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems. Computing 99(12):1231–1255
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
Wang W, Liang B, Li B (2015) Multi-resource fair allocation in heterogeneous cloud computing systems. IEEE Transact Parall Distrib Syst 26(10):2822–2835
Tan J, Zhang L, Li M et al (2015) Multi-resource fair sharing for multiclass workflows. Perfoma valuat Rev 42(4):31–37
Sadok H, Campista MEM, Costa LHMK (2021) Stateful drf: Considering the past in a multi-resource allocation. IEEE Trans Comput 70(7):1094–1105
Poullie P, Mannhart S, Stiller B (2016) Virtual machine priority adaption to enforce fairness among cloud users. In: Proceedings of the 12th Conference on International Conference on Network and Service Management, pp 91–99
Lan T, Kao D, Chiang M, et al (2010) An axiomatic theory of fairness in network resource allocation. In: 2010 Proceedings IEEE INFOCOM, pp 1–9
Li W, Liu X, Zhang X, et al (2017) A further analysis of the dynamic dominant resource fairness mechanism. In: International Workshop on Frontiers in Algorithmics, pp 163–174
Grandl R, Ananthanarayanan G, Kandula S et al (2014) Multi-resource packing for cluster schedulers. SIGCOMM Comput Commun Rev 44(4):455–466
Kash I, Procaccia AD, Shah N (2014) No agent left behind: dynamic fair division of multiple resources. J Artif Intell Res 51(1):579–603
Li J, Xue J (2013) Egalitarian division under leontief preferences. Econ Theory 54(3):597–622
Parkes DC, Procaccia AD, Shah N (2015) Beyond dominant resource fairness: Extensions, limitations, and indivisibilities. Assoc Comput Mach 3(1):1–22
IBM (2022) IBM ILOG CPLEX Optimizer. Available online: https://www.ibm.com/analytics/cplex-optimizer (accessed on 21 October 2022)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China [Nos. 12071417, 62266051 and 62062065] and the 14th Postgraduate Innovation Project of Yunnan University [No. KC-22223141].
Author information
Authors and Affiliations
Corresponding author
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.
About this article
Cite this article
Li, X., Li, W. & Zhang, X. Extended efficiency and soft-fairness multiresource allocation in a cloud computing system. Computing 105, 1217–1245 (2023). https://doi.org/10.1007/s00607-022-01138-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-022-01138-6