Economics-Driven Software Architecting for Cloud

Many of the problems facing providers and users of Cloud-based systems, in terms of maximizing Quality-of-Service (QoS) satisfaction, can be studied using theories in microeconomics. Specifically, the concept of market-based control provides tools that can be used to design economically and computationally efficient Cloud software architectures. This chapter surveys both domains and presents some of the underlying problems and opportunities in the interesting crossbreed of economics and Cloud computing. The dynamic resource allocation problem is used as an example to demonstrate the added value of this approach. Observations from simulation studies reveal the usefulness of the posted offer market model as a viable mechanism for orchestrating the interaction of components in a Cloud software architecture. The chapter concludes with a reflection on open problems that need to be addressed to move the area forward.

[1]  Biao Song,et al.  A Novel Heuristic-Based Task Selection and Allocation Framework in Dynamic Collaborative Cloud Service Platform , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[2]  Xianghua Xu,et al.  RAS-M: Resource Allocation Strategy Based on Market Mechanism in Cloud Computing , 2009, 2009 Fourth ChinaGrid Annual Conference.

[3]  Richard Wolski,et al.  Analyzing Market-Based Resource Allocation Strategies for the Computational Grid , 2001, Int. J. High Perform. Comput. Appl..

[4]  Rami Bahsoon,et al.  A decentralized self-adaptation mechanism for service-based applications in the cloud , 2013, IEEE Transactions on Software Engineering.

[5]  Nicolas Guelfi,et al.  A Generic Framework for the Engineering of Self-Adaptive and Self-Organising Systems , 2008, Organic Computing - Controlled Self-organization.

[6]  Akbar Ghaffarpour Rahbar,et al.  PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing , 2007, IEEE Transactions on Parallel and Distributed Systems.

[7]  Bradley R. Schmerl,et al.  Software Engineering for Self-Adaptive Systems: A Second Research Roadmap , 2010, Software Engineering for Self-Adaptive Systems.

[8]  Guiran Chang,et al.  Efficient Nash equilibrium based cloud resource allocation by using a continuous double auction , 2010, 2010 International Conference On Computer Design and Applications.

[9]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[10]  Sarvapali D. Ramchurn,et al.  Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty , 2009, J. Artif. Intell. Res..

[11]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[12]  Bo Hong,et al.  Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[13]  Vernon L. Smith,et al.  A Comparison of Posted-Offer and Double-Auction Pricing Institutions , 1984 .

[14]  Rajkumar Buyya,et al.  Market-Oriented Cloud Computing: Vision, Hype, and Reality of Delivering Computing as the 5th Utility , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[15]  Barbara Panicucci,et al.  A game theoretic formulation of the service provisioning problem in cloud systems , 2011, WWW.

[16]  Sarvapali D. Ramchurn,et al.  Trust-based mechanism design , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[17]  Jeff Magee,et al.  Self-Managed Systems: an Architectural Challenge , 2007, Future of Software Engineering (FOSE '07).

[18]  Guangwen Yang,et al.  A Knowledge-based Continuous Double Auction Model for Cloud Market , 2010, 2010 Sixth International Conference on Semantics, Knowledge and Grids.

[19]  Albert Y. Zomaya,et al.  Profit-Driven Service Request Scheduling in Clouds , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[20]  Mark Klein,et al.  Auctions and bidding: A guide for computer scientists , 2011, CSUR.

[21]  Isao Ono,et al.  Applying Double-Sided Combinational Auctions to Resource Allocation in Cloud Computing , 2010, 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet.

[22]  Kevin Lai,et al.  Markets are dead, long live markets , 2005, SECO.

[23]  Mary Shaw,et al.  Software Engineering for Self-Adaptive Systems: A Research Roadmap , 2009, Software Engineering for Self-Adaptive Systems.

[24]  Michael Wooldridge,et al.  Agent-Oriented Software Engineering , 1999, ATAL.

[25]  Jeff Magee,et al.  Self-organising software architectures for distributed systems , 2002, WOSS '02.

[26]  Andy Evans,et al.  Evaluating Security Properties of Architectures in Unpredictable Environments: A Case for Cloud , 2011, 2011 Ninth Working IEEE/IFIP Conference on Software Architecture.

[27]  Rami Bahsoon,et al.  Self-managing SLA compliance in cloud architectures: a market-based approach , 2012, ISARCS '12.

[28]  Rajkumar Buyya,et al.  SLA-Based Coordinated Superscheduling Scheme for Computational Grids , 2006, 2006 IEEE International Conference on Cluster Computing.

[29]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[30]  Rami Bahsoon,et al.  A Dynamic Data-Driven Simulation Approach for Preventing Service Level Agreement Violations in Cloud Federation , 2012, ICCS.

[31]  Xin Yao,et al.  Resource allocation in decentralised computational systems: an evolutionary market-based approach , 2009, Autonomous Agents and Multi-Agent Systems.

[32]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[33]  Tuomas Sandholm,et al.  Distributed rational decision making , 1999 .

[34]  P. Klemperer Auction Theory: A Guide to the Literature , 1999 .