Abstract
Infrastructure-as-a-service consumers are presented with numerous Cloud providers with a wide variety of resources. However, consumers are faced with providers that may offer (even similar) resources at different hourly cost rates, and also that no single provider may have matching resource capabilities to fulfill a highly heterogeneous set of requirements. This work proposes an agent-based approach endowed with the well-known contract net protocol for allocating heterogeneous resources from multiple Cloud providers while selecting the most economical resources. The contributions of this paper are: (i) devising an agent-based architecture for resource allocation in multi-Cloud environments, and (ii) implementing the agent-based Cloud resource allocation mechanism in commercial Clouds using Amazon EC2 as a case study. The Amazon EC2 case study shows that agents can autonomously select and allocate heterogeneous resources from multiple Cloud providers while dynamically sampling resources’ allocation cost for selecting the most economical resources.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amazon EC2 API Tools, http://aws.amazon.com/developertools/351
Amazon EC2 FAQs, http://aws.amazon.com/ec2/faqs
Amazon Elastic Compute Cloud (Amazon EC2), http://aws.amazon.com/ec2
Asmild, M., Paradi, J.C., Pastor, J.T.: Centralized Resource Allocation BCC Models. Omega 37(1), 40–49 (2009)
AWS SDK for Java – A Java Library for Amazon S3, Amazon EC2, and More, http://aws.amazon.com/sdkforjava
Bellifemine, F., Poggi, A., Rimassa, G.: JADE - A FIPA-Compliant Agent Framework. In: 4th International Conference and Exhibition on the Practical Application of Intelligent Agents and Multi-Agents, pp. 97–108 (1999)
Bouncy Castle Crypto APIs, http://www.bouncycastle.org
Buyya, R., Pandey, S., Vecchiola, C.: Cloudbus Toolkit for Market-Oriented Cloud Computing. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing. LNCS, vol. 5931, pp. 24–44. Springer, Heidelberg (2009)
GoGrid, http://www.gogrid.com
Lee, K., Paton, N.W., Sakellariou, R., Deelman, E., Fernandes, A.A.A., Metha, G.: Adaptive Workflow Processing and Execution in Pegasus. Concurr. Comput.: Pract. Exper. 21(16), 1965–1981 (2009)
RackSpace, http://www.rackspace.com
Sim, K.M.: A Survey of Bargaining Models for Grid Resource Allocation. SIGecom Exch. 5(5), 22–32 (2006)
Smith, R.G.: The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Trans. Comput. 29(12), 1104–1113 (1980)
Yang, Y., Liu, K., Chen, J., Lignier, J., Jin, H.: Peer-to-peer Based Grid Workflow Runtime Environment of SwinDeW-G. In: 3rd IEEE International Conference on e-Science and Grid Computing, pp. 51–58. IEEE Computer Society, Washington (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gutierrez-Garcia, J.O., Sim, K.M. (2011). Agents for Cloud Resource Allocation: An Amazon EC2 Case Study. In: Kim, Th., et al. Grid and Distributed Computing. GDC 2011. Communications in Computer and Information Science, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27180-9_66
Download citation
DOI: https://doi.org/10.1007/978-3-642-27180-9_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27179-3
Online ISBN: 978-3-642-27180-9
eBook Packages: Computer ScienceComputer Science (R0)