The Community for Technology Leaders
RSS Icon
Issue No.01 - Jan. (2014 vol.25)
pp: 2-11
Shaojie Tang , Temple Univ., Philadelphia, PA, USA
Jing Yuan , Dept. of Comput. Sci., Univ. of Illinois, Philadelphia, PA, USA
Cheng Wang , Dept. of Comput. Sci. & Eng., Tongji Univ., Shanghai, China
Xiang-Yang Li , Dept. of Comput. Sci., Illinois Inst. of Technol., Chicago, IL, USA
With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and, thus, control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability tradeoffs under this scheme are of great value for users seeking to reduce their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies under several service-level agreement (SLA) constraints. In particular, we aim to minimize the monetary cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance price traces and workload models, we compare several adaptive checkpointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.
Cloud computing, Resource management, Contracts,EC2, Cloud computing, bidding strategy
Shaojie Tang, Jing Yuan, Cheng Wang, Xiang-Yang Li, "A Framework for Amazon EC2 Bidding Strategy under SLA Constraints", IEEE Transactions on Parallel & Distributed Systems, vol.25, no. 1, pp. 2-11, Jan. 2014, doi:10.1109/TPDS.2013.15
[1] Amazon EC2 Spot Instances, #instance, 2013.
[2] Amazon Simple Storage Service FAQs,, 2013.
[3] CloudKick: Simple, Powerful Tools to Manage and Monitor Cloud Servers, http:/, 2013.
[4] CloudStatus, http:/, 2013.
[5] http:/, 2013.
[6] RightScale: Cloud Computing Management Platform, http:/, 2013.
[7] E. Altman, Constrained Markov Decision Processes, vol. 7. CRC Press, 1999.
[8] A. Andrzejak, D. Kondo, and D. Anderson, "Exploiting Non-Dedicated Resources for Cloud Computing," Proc. IEEE Network Operations and Management Symp. (NOMS '10), 2010.
[9] A. Andrzejak, D. Kondo, and S. Yi, "Decision Model for Cloud Computing under SLA Constraints," Proc. 18th Ann. IEEE/ACM Int'l Symp. Modeling, Analysis and Simulation of Computer and Telecomm. Systems, pp. 257-266, 2010.
[10] J. Dean and S. Ghemawat, "Mapreduce: Simplified Data Processing on Large Clusters," Comm. ACM, vol. 51, pp. 107-113, 2008.
[11] E. Deelman, G. Singh, M. Livny, B. Berriman, and J. Good, "The Cost of Doing Science on the Cloud: The Montage Example," Proc. ACM/IEEE Conf. Supercomputing, p. 50, 2008.
[12] S. Garfinkel, Commodity Grid Computing with Amazon's s3 and ec2, Defense Technical Information Center, 2007.
[13] A. Iosup, O. Sonmez, S. Anoep, and D. Epema, "The Performance of Bags-of-Tasks in Large-Scale Distributed Systems," Proc. 17th ACM Int'l Symp. High Performance Distributed Computing, pp. 97-108, 2008.
[14] D. Kondo, B. Javadi, P. Malecot, F. Cappello, and D. Anderson, "Cost-Benefit Analysis of Cloud Computing versus Desktop Grids," Proc. IEEE Int'l Symp. Parallel and Distributed Processing (IPDPS '09), 2009.
[15] M. Litzkow, M. Livny, and M. Mutka, "Condor-a Hunter of Idle Workstations," Proc. IEEE Eighth Int'l Conf. Distributed Computing Systems, pp. 104-111, 1988.
[16] M. Plankar, A. Iamnitchi, M. Ripeanu, and S. Garfinkel, "Amazon S3 for Science Grids: A Viable Solution," Proc. Int'l Workshop Data-Aware Distributed Computing (DADC '08), 2008.
[17] M. Stokely, J. Winget, C. Keyes, and B. Yolken, "Using a Market Economy to Provision Compute Resources across Planet-Wide Clusters," Proc. IEEE Int'l Symp. Parallel and Distributed Processing Symp., 2009.
[18] S. Tang, J. Yuan, and X. Li, "Towards Optimal Bidding Strategy for Amazon Ec2 Cloud Spot Instance," Proc. IEEE Fifth Int'l Conf. Cloud Computing, 2012.
[19] W. Wang, B. Li, and B. Liang, "Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing," technical report, Univ. of Toronto, 2011.
[20] S. Yi, J. Heo, Y. Cho, and J. Hong, "Adaptive Page-Level Incremental Checkpointing Based on Expected Recovery Time," Proc. ACM Symp. Applied Computing, pp. 1472-1476, 2006.
[21] S. Yi, J. Heo, Y. Cho, and J. Hong, "Taking Point Decision Mechanism for Page-Level Incremental Checkpointing Based on Cost Analysis of Process Execution Time," J. Information Science and Eng., vol. 23, pp. 1325-1337, 2007.
[22] S. Yi, D. Kondo, and A. Andrzejak, "Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud," Proc. IEEE Third Int'l Conf. Cloud Computing, pp. 236-243, 2010.
98 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool