Using a Predator-Prey Model to Explain Variations of Cloud Spot Price
Zheng Li, William Tärneberg, Maria Kihl, Anders Robertsson
2016
Abstract
The spot pricing scheme has been considered to be resource-efficient for providers and cost-effective for consumers in the Cloud market. Nevertheless, unlike the static and straightforward strategies of trading on-demand and reserved Cloud services, the market-driven mechanism for trading spot service would be complicated for both implementation and understanding. The largely invisible market activities and their complex interactions could especially make Cloud consumers hesitate to enter the spot market. To reduce the complexity in understanding the Cloud spot market, we decided to reveal the backend information behind spot price variations. Inspired by the methodology of reverse engineering, we developed a Predator-Prey model that can simulate the interactions between demand and resource based on the visible spot price traces. The simulation results have shown some basic regular patterns of market activities with respect to Amazon’s spot instance type m3.large. Although the findings of this study need further validation by using practical data, our work essentially suggests a promising approach (i.e. using a Predator-Prey model) to investigate spot market activities.
References
- Abhishek, V., Kash, I. A., and Key, P. (2012). Fixed and market pricing for Cloud services. In Proc. 7th Workshop on the Economics of Networks, Systems, and Computation (NetEcon 2012), pages 157-162, Orlando, FL, USA. IEEE Computer Society.
- Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., and Ahmad, I. (2013). Cloud computing pricing models: A survey. International Journal of Grid and Distributed Computing, 6(5):93-106.
- Amazon (2015a). Amazon EC2 spot instances. https://aws.amazon.com/ec2/purchasing-options/spotinstances/.
- Amazon (2015b). ec2-describe-spot-pricehistory. http:// docs.aws.amazon.com/AWSEC2/latest/CommandLine Reference/ApiReference-cmd-DescribeSpotPrice History.html.
- Åström, K. J. and Murray, R. M. (2008). Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, Princeton, New Jersey.
- Berryman, A. A. (1992). The origins and evolution of predator-prey theory. Ecology, 73(5):1530-1535.
- Braha, D. (2012). Global civil unrest: Contagion, selforganization, and prediction. PLoS ONE, 7(12):1-9.
- Chaisiri, S., Kaewpuang, R., Lee, B.-S., and Niyato, D. (2011). Cost minimization for provisioning virtual servers in Amazon elastic compute Cloud. In Proc. 19th Ann. IEEE Int. Symp. Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2011), pages 85-95, Singapore. IEEE Computer Society.
- Chohan, N., Castillo, C., Spreitzer, M., Steinder, M., Tantawi, A., and Krintz, C. (2010). See spot run: Using spot instances for MapReduce workflows. In Proc. 2nd USENIX conf. Hot topics in cloud computing (HotCloud 2010), pages 1-7, Boston, MA, USA. USENIX Association.
- Delimitrou, C. and Kozyrakis, C. (2014). Quasar: Resource-efficient and QoS-aware cluster management. In Proc. 19th Int. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS 2014), pages 127-144, Salt Lake City, Utah, USA. ACM Press.
- Di Valerio, V., Cardellini, V., and Lo Presti, F. (2013). Optimal pricing and service provisioning strategies in Cloud systems: A Stackelberg game approach. In Proc. 6th IEEE Int. Conf. Cloud Computing (CLOUD 2013), pages 115-122, Santa Clara, CA, USA. IEEE Computer Society.
- Guo, W., Chen, K., Wu, Y., and Zheng, W. (2015). Bidding for highly available services with low price in spot instance market. In Proc. 24th Int. ACM Symp. HighPerformance Parallel and Distributed Computing (HPDC 2015), pages 191-202, Portland, Oregon, USA. ACM Press.
- Jangjaimon, I. and Tzeng, N.-F. (2015). Effective cost reduction for elastic Clouds under spot instance pricing through adaptive checkpointing. IEEE Transactions on Computers, 64(2): 396-409.
- Kantere, V., Dash, D., Franc¸ois, G., Kyriakopoulou, S., and Ailamaki, A. (2011). Optimal service pricing for a Cloud cache. IEEE Transactions on Knowledge and Data Engineering, 23(9):1345-1358.
- Karunakaran, S. and Sundarraj, R. P. (2013). On using prisoner dilemma model to explain bidding decision for computing resources on the Cloud. In Proc. 13th Int. Conf. Group Decision and Negotiation (GDN 2013), pages 206-215, Stockholm, Sweden.
- Li, Z., Zhang, H., O'Brien, L., Jiang, S., Zhou, Y., Kihl, M., and Ranjan, R. (2016). Spot pricing in the Cloud ecosystem: A comparative investigation. Journal of Systems and Software, 114: 1-19.
- Mazzucco, M. and Dumas, M. (2011). Achieving performance and availability guarantees with spot instances. In Proc. 13th IEEE Int. Conf. High Performance Computing and Communications (HPCC 2011), pages 296-303, Banff, Canada. IEEE Computer Society.
- Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf's law. Contemporary Physics, 46(5):323-351.
- Puschel, T., Borissov, N., Macías, M., Neumann, D., Guitart, J., and Torres, J. (2007). Economically enhanced resource management for Internet service utilities. In Proc. 8th Int. Conf. Web Information Systems Engineering (WISE 2007), pages 335-348, Nancy, France. Springer-Verlag.
- Shi, W., Zhang, L., Wu, C., Li, Z., and Lau, F. C. (2014). An online auction framework for dynamic resource provisioning in Cloud computing. In Proc. 2014 ACM Int. Conf. Measurement and Modeling of Computer Systems (SIGMETRICS 2014), pages 71-83, Austin, Texas, USA. ACM Press.
- v. Kistowski, J., Herbst, N., and Kounev, S. (2014). Modeling variations in load intensity over time. In Proc. 3rd Int. Workshop on Large Scale Testing (LT 2014), pages 1-4, Dublin Ireland. ACM Press.
- Wang, P., Qi, Y., Hui, D., Rao, L., and Lin, X. (2013). Present or future: Optimal pricing for spot instances. In Proc. 33rd Int. Conf. Distributed Computing Systems (ICDCS 2013), pages 410-419, Philadelphia, USA. IEEE Computer Society.
- Wee, S. (2011). Debunking real-time pricing in Cloud computing. In Proc. 11th IEEE/ACM Int. Symp. Cluster, Cloud and Grid Computing (CCGrid 2011), pages 585-590, Newport Beach, CA, USA. IEEE Computer Society.
- Wescott, B. (2013). Every Computer Performance Book: How to Avoid and Solve Performance Problems on The Computers You Work With. CreateSpace Independent Publishing Platform.
- Xu, H. and Li, B. (2013). Dynamic Cloud pricing for revenue maximization. IEEE Transactions on Cloud Computing, 1(2):158-171.
- Zaman, S. and Grosu, D. (2011). Efficient bidding for virtual machine instances in Clouds. In Proc. 4th IEEE Int. Conf. Cloud Computing (CLOUD 2011), pages 41- 48, Washington, DC, USA. IEEE Computer Society.
Paper Citation
in Harvard Style
Li Z., Tärneberg W., Kihl M. and Robertsson A. (2016). Using a Predator-Prey Model to Explain Variations of Cloud Spot Price . In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 2: CLOSER, ISBN 978-989-758-182-3, pages 51-58. DOI: 10.5220/0005808600510058
in Bibtex Style
@conference{closer16,
author={Zheng Li and William Tärneberg and Maria Kihl and Anders Robertsson},
title={Using a Predator-Prey Model to Explain Variations of Cloud Spot Price},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 2: CLOSER,},
year={2016},
pages={51-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005808600510058},
isbn={978-989-758-182-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 2: CLOSER,
TI - Using a Predator-Prey Model to Explain Variations of Cloud Spot Price
SN - 978-989-758-182-3
AU - Li Z.
AU - Tärneberg W.
AU - Kihl M.
AU - Robertsson A.
PY - 2016
SP - 51
EP - 58
DO - 10.5220/0005808600510058