Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance
Authors: 
Xiaohu Wu, Francesco De Pellegrini, and Giuliano CasaleAuthors Info & Claims
Xiaohu Wu, Francesco De Pellegrini, and Giuliano CasaleAuthors Info & ClaimsArticle No.: 6, Pages 1 - 40
Abstract
Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.
References
[1]
Gartner Says Worldwide IaaS Public Cloud Services Market Grew 41.4% in 2021. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2022-06-02-gartner-says-worldwide-iaas-public-cloud-services-market-grew-41-percent-in-2021.
[2]
R. Buyya, S. N. Srirama, G. Casale, and R. Calheiros, et al. 2019. A manifesto for future generation cloud computing: Research directions for the next decade. ACM Comput. Surv. 51, 5, Article 105 (January 2019), 38 pages.
[3]
D. Bertsekas and R. Gallager. 1987. In Data Networks. Prentice-Hall, Upper Saddle River, NJ.
[4]
R. Zhou, Z. Li, C. Wu, and Z. Huang. 2016. An efficient cloud market mechanism for computing jobs with soft deadlines. IEEE/ACM Trans. Network. 25, 2 (2016), 793–805.
[5]
Testimonials and Case Studies. Retrieved November 20, 2022 from https://aws.amazon.com/ec2/spot/testimonials/.
[6]
Orna Agmon Ben-Yehuda, Muli Ben-Yehuda, Assaf Schuster, and Dan Tsafrir. 2013. Deconstructing Amazon EC2 Spot Instance Pricing. ACM Trans. Econ. Comput. 1, 3 (2013), 20 pages.
[7]
D. Ardagna, G. Casale, M. Ciavotta, J. F Pérez, and W. Wang. 2014. Quality-of-service in cloud computing: Modeling techniques and their applications. J. Internet Serv. Appl. 5(1) (2014), 1–17.
[8]
J. Anselmi, D. Ardagna, John C. S. Lui, Adam Wierman, Y. Xu, and Z. Yang. 2017. The economics of the cloud. ACM Trans. Model. Perf. Eval. Comput. Syst. 2, 4, Article 18 (December 2017), 23 pages.
[9]
V. Abhishek, I. Kash, and P. Key. 2012. Fixed and market pricing for cloud services. In Proceedings of the 7th Workshop on the Economics of Networks, Systems and Computation (NetEcon’12), IEEE, 157–162.
[10]
L. Dierks, and S. Seuken. 2019. Cloud pricing: The spot market strikes back. In Proceedings of the ACM Conference on Economics and Computation (EC’19), ACM, 593–593.
[11]
L. Dierks and S. Seuken. 2022. Cloud pricing: The spot market strikes back. Manage, Sci, 68, 1 (2022), 105–122.
[12]
C. Kilcioglu and C. Maglaras. 2015. Revenue maximization for cloud computing services. SIGMETRICS Perform. Eval, Rev. 43, 3 (2015), 76.
[13]
S. Chen, K. Moinzadeh, and Y. Tan. 2021. Discount schemes for the preemptible service of a cloud platform with unutilized capacity. Inf. Syst. Res. 32, 3 (2021), 967–986.
[14]
X. Wu, F. De Pellegrini, G. Gao, and G. Casale. 2019. A framework for allocating server time to spot and on-demand services in cloud computing. ACM Trans. Model. Perf. Eval. Comput. Syst. 4, 4, Article 20 (2019), 31 pages.
[15]
J. Song and R. Guérin. 2020. Pricing (and bidding) strategies for delay differentiated cloud services. ACM Trans. Econ. Comput. 8, 2, Article 8 (May 2020), 58 pages.
[16]
S. Boodaghians, F. Fusco, S. Leonardi, Y. Mansour, and R. Mehta. 2020. Online revenue maximization for server pricing. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence (IJCAI’20). 4106–4112.
[17]
S. K. Garg, A. Toosi, S. K. Gopalaiyengar, and R. Buyya. 2014. SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J. Netw. Comput. Appl. 45 (2014), 108–120.
[18]
K. Psychas and J. Ghaderi. On non-preemptive VM scheduling in the cloud. In Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS’17), 1, 2, Article 35 (2017), 29 pages.
[19]
W. Dargie. 2014. Estimation of the cost of VM migration. In Proceedings of the 23rd International Conference on Computer Communication and Networks (ICCCN’14), IEEE, 1–8.
[20]
I. Kash and P. Key. 2016. Pricing the cloud. IEEE Internet Comput. 20, 1 (2016), 36–43.
[21]
S. Yi, D. Kondo, and A. Andrzejak. Reducing costs of spot instances via checkpointing in the Amazon elastic compute cloud. In Proceedings of the IEEE 3rd International Conference on Cloud Computing (CLOUD’10). IEEE Computer Society, Los Alamitos, CA, 236–243.
[22]
J. C. S. Kadupitige, V. Jadhao, and P. Sharma. Modeling the temporally constrained preemptions of transient cloud VMs. In Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (HPDC’20), ACM, New York, NY, 41–52.
[23]
N. Nisan, T. Roughgarden, Éva Tardos, and V. Vazirani. 2007. In Algorithmic Game Theory, Cambridge University Press.
[24]
N. R. Devanur. 2017. A report on the workshop on the economics of cloud computing. ACM SIGecom Exch. 15.2 (2017), 25–29.
[25]
X. Wu, P. Loiseau, and E. Hyytiä. 2020. Towards designing cost-optimal policies to utilize IaaS clouds with online learning. IEEE Transactions on. Parallel Distrib. Syst. 31, 3 (2020), 501–514.
[26]
D. J. Dubois and G. Casale. 2016. OptiSpot: Minimizing application deployment cost using spot cloud resources. Cluster Comput. 19, 2 (2016), 893–909.
[27]
X. Wu, H. Yu, G. Casale, and G. Gao. 2021. Towards cost-optimal policies for DAGs to utilize IaaS clouds with online learning.arXiv:2106.01847. Retrieved from https://arxiv.org/abs/2106.01847.
[28]
Y. Azar, I. Kalp-Shaltiel, B. Lucier, I. Menache, J. Naor, and J. Yaniv. 2015. Truthful online scheduling with commitments. In Proceedings of the 16th ACM Conference on Economics and Computation (EC’15). ACM, 715–732.
[29]
N. Jain, I. Menache, J. Naor, and J. Yaniv. 2015. Near-optimal scheduling mechanisms for deadline-sensitive jobs in large computing clusters. ACM Trans. Parallel Comput. 2, 1 (2015), 3.
[30]
X. Wu and P. Loiseau. 2015. Algorithms for scheduling deadline-sensitive malleable tasks. In Proceedings of the 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton’15). IEEE, 530–537.
[31]
X. Zhang, Z. Huang, C. Wu, Z. Li, and F. C. M. Lau. 2015. Online auctions in IaaS clouds: Welfare and profit maximization with server costs. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS’15). ACM, 3–15.
[32]
W. Shi, L. Zhang, C. Wu, Z. Li, and F. C. M. Lau. 2014. An online auction framework for dynamic resource provisioning in cloud computing. In Proceedings of the ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS’14). ACM, New York, NY, 71–83.
[33]
C. S. Yeo and R. Buyya. 2005. Service level agreement based allocation of cluster resources: Handling penalty to enhance utility. In Proceedings of the IEEE International Conference on Cluster Computing. 1–10.
[34]
J. Rasley, K. Karanasos, S. Kandula, R. Fonseca, M. Vojnovic, and S. Rao. 2016. Efficient queue management for cluster scheduling. In Proceedings of the 11th European Conference on Computer Systems (EuroSys’16). ACM, 36.
[35]
L. Zheng, C. Joe-Wong, C. Brinton, C.-W. Tan, S. Ha, and M. Chiang. 2016. On the viability of a cloud virtual service provider. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS’16). ACM.
[36]
W. Wang and G. Casale. 2014. Evaluating weighted round robin load balancing for cloud web services. In Proceedings of the 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC’14), IEEE, 393–400.
[37]
E. Hyytiä and S. Aalto. 2016. On Round-Robin routing with FCFS and LCFS scheduling. Perf. Eval. 97 (2016), 83–103.
[38]
A. Chung, J. W. Park, and G. R. Ganger. 2018. Stratus: Cost-aware container scheduling in the public cloud. In Proceedings of the ACM Symposium on Cloud Computing (SoCC’18). ACM, 121–134.
[39]
K. Karanasos, S. Rao, C. Curino, C. Douglas, K. Chaliparambil, G. M. Fumarola, S. Heddaya, R. Ramakrishnan, and S. Sakalanaga. 2015. Mercury: Hybrid centralized and distributed scheduling in large shared clusters. In Proceedings of the USENIX Annual Technical Conference (ATC’15). USENIX Association, 485–497.
[40]
D. Mukherjee, S. Dhara, Sem C. Borst, and J. S. H. van Leeuwaarden. 2017. Optimal service elasticity in large-scale distributed systems. In Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS’17), 1, 1, Article 25, 28 pages.
[41]
L. Zheng, C. Joe-Wong, C. W. Tan, M. Chiang, and X. Wang. How to bid the cloud. In Proceedings of the ACM Conference on Special Interest Group on Data Communication (SIGCOMM’15). ACM, 71–84.
[42]
S. Sen, Y. Jin, R. Guérin, and K. Hosanagar. 2010. Modeling the dynamics of network technology adoption and the role of converters. IEEE/ACM Trans. Netw. 18, 6 (December 2010), 1793–1805.
[43]
K. Ousterhout, P. Wendell, M. Zaharia, and I. Stoica. 2013. Sparrow: Distributed, low latency scheduling. In Proceedings of the 24th ACM Symposium on Operating Systems Principles (SOSP’13). ACM, 69–84
[44]
M. Bertoli, G. Casale, and G. Serazzi. 2009. JMT: performance engineering tools for system modeling. SIGMETRICS Perf. Eval. Rev. 36, 4 (March 2009), 10–15.
[45]
M. Mitzenmacher. 2001. The power of two choices in randomized load balancing. IEEE Trans. Parallel Distrib. Syst. 12, 10 (October 2001), 1094–1104.
[46]
W. M. Pride, O. C. Ferrell, B. Lukas, S. Schembri, and O. Niininen. 2017. Chapter 6: Segmentations, target markets and positioning. In Marketing Principles. Cengage Learning Australia, Melbourne.
[47]
Peter R. Dickson and James L. Ginter. 1987. Market segmentation, product differentiation, and marketing strategy. J. Market. 51, 2 (1987), 1–10.
[48]
L. Dierks, I. A. Kash, and S. Seuken. On the cluster admission problem for cloud computing. In Proceedings of the 14th Workshop on the Economics of Networks, Systems and Computation (NetEcon’19). ACM, New York, NY, 1–6.
[49]
L. Dierks and S. Seuken. 2020. The competitive effects of variance-based pricing. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI’20). 362–370.
[50]
Paul W. Farris, Neil Bendle, Phillip E. Pfeifer, and David Reibstein. 2010. In Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Pearson Education.
[51]
K. T. Talluri and G. V. Ryzin. 2004. In The Theory and Practice of Revenue Management, Vol. 1. Kluwer Academic, Boston.
[52]
Y. Braouezec. 2012. Customer-class pricing, parallel trade and the optimal number of market segments. Int. J. Industr. Org. 30, 6 (2012), 605–614.
[53]
K. Sridhar Moorthy. 1984. Market segmentation, self-selection, and product line design. Market. Sci. 3, 4 (1984), 288–307.
[54]
J. Nair, A. Wierman, and B. Zwart. 2022. In The Fundamentals of Heavy Tails, Vol. 53. Cambridge University Press.
[55]
J. Almeida, V. Almeida, D. Ardagna, I. Cunha, C. Francalanci, and M. Trubian. 2010. Joint admission control and resource allocation in virtualized servers. J. Parallel Distrib. Comput. 70, 4 (2010), 344–362.
[56]
M. Mazzucco and M. Dumas. 2011. Achieving performance and availability guarantees with spot instances. In Proceedings of the IEEE International Conference on High Performance Computing and Communications. IEEE, 296–303.
[57]
Hui Li, Giuliano Casale, and Tariq Ellahi. 2010. SLA-driven planning and optimization of enterprise applications. In Proceedings of the 1st Joint WOSP/SIPEW International Conference on Performance Engineering. 117–128.
Index Terms
- Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance
Recommendations
Auction-based cloud service differentiation with service level objectives
We present a new study on service differentiation techniques for general cloud system. Our solution potentially opens new business models for cloud systems in the future, and enables ordinary users to exploit the benefits of clouds.We propose Abacus an ...
Price optimisation of cloud computing service in a monopolistic competitive market
We present an analytical study on services price competition in the cloud computing monopolistic markets. Our aim is to prove the existence of an equilibrium price in such competitive and non-cooperative markets, which maximizes the social welfare by the ...
Optimal Price and Delay Differentiation in Large-Scale Queueing Systems
We study a multiserver queueing model of a revenue-maximizing firm providing a service to a market of heterogeneous price-and delay-sensitive customers with private individual preferences. The firm may offer a selection of service classes that are ...
Comments
Information & Contributors
Information
Published In
September 2023
140 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 June 2023
Online AM: 17 April 2023
Accepted: 07 April 2023
Revised: 05 April 2023
Received: 20 April 2022
Published in TOMPECS Volume 8, Issue 3
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 231Total Downloads
- Downloads (Last 12 months)165
- Downloads (Last 6 weeks)13
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in