A general model for resource allocation in utility computing

A utility computing problem is one in which a server (service provider) provides computing resources to clients (service receivers) whose jobs require the resources for processing. In this paper, we propose a general, decentralized and auction-based model for the server-to-clients resource allocation problem. This model combines a general class of queueing processes with a general class of “incentivecompatible” bidding mechanisms. Crucial to this model is the interplay between the nature of queueing costs and the nature of good bidding mechanisms. Insights on how such interplay contributes to the stability of the system can be helpful in guiding specific implementations in real-world applications. This decentralized and auction-based resource allocation model naturally induces a multi-player game, which is the principal object we study in this paper. We show the existence and uniqueness of Nash equilibrium under this general setting. We also present distributed update dynamics that converge to this unique Nash equilibrium. The distributed dynamics given here share the features of being secure and requiring little communications, thus, providing a practical scheme through which Nash equilibrium is reached.

[1]  Roy D. Yates,et al.  A Framework for Uplink Power Control in Cellular Radio Systems , 1995, IEEE J. Sel. Areas Commun..

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

[3]  Ulrich Drepper,et al.  The Cost of Virtualization , 2008, ACM Queue.

[4]  David E. Culler,et al.  Market-based Proportional Resource Sharing for Clusters , 2000 .

[5]  Nicholas Bambos,et al.  Game based capacity allocation for utility computing environments , 2008, Valuetools 2008.

[6]  Leslie R. Fine,et al.  Tycoon: A Market-Based Resource Allocation System , 2004 .

[7]  Hussein M. Abdel-Wahab,et al.  A proportional share resource allocation algorithm for real-time, time-shared systems , 1996, 17th IEEE Real-Time Systems Symposium.

[8]  Barbara Panicucci,et al.  Generalized Nash Equilibria for the Service Provisioning Problem in Cloud Systems , 2013, IEEE Transactions on Services Computing.

[9]  Y. Mansour,et al.  Algorithmic Game Theory: Learning, Regret Minimization, and Equilibria , 2007 .

[10]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[11]  Michael A. Rappa,et al.  The utility business model and the future of computing services , 2004, IBM Syst. J..

[12]  Sanjeev Arora,et al.  The Multiplicative Weights Update Method: a Meta-Algorithm and Applications , 2012, Theory Comput..

[13]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[14]  Bernardo A. Huberman,et al.  Tycoon: A Market-Based Resource Allocation System , 2004, ArXiv.

[15]  J. Walrand,et al.  Mechanisms for Efficient Allocation in Divisible Capacity Networks , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[16]  Asuman E. Ozdaglar,et al.  Socially optimal pricing of cloud computing resources , 2011, VALUETOOLS.

[17]  Y. Mansour,et al.  4 Learning , Regret minimization , and Equilibria , 2006 .

[18]  John N. Tsitsiklis,et al.  Efficiency loss in a network resource allocation game: the case of elastic supply , 2005, IEEE Trans. Autom. Control..

[19]  Brian Duncan.  Pumpkin Pies and Public Goods: The Raffle Fundraising Strategy , 2002 .

[20]  Peng Xu,et al.  Multi-dimensional Resource Integrated Scheduling in a Shared Data Center , 2011, 2011 31st International Conference on Distributed Computing Systems Workshops.

[21]  Tamer Basar,et al.  Auctions for Divisible Resources: Price Functions, Nash Equilibrium, and Decentralized Update Schemes , 2002, AMEC.

[22]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .