On the Importance of Migration for Fairness in Online Grid Markets (Short Paper)

Computational grids oer users a simple access to tremendous computer resources for solving large scale computing problems. Traditional performance analysis of scheduling algorithms considers overall system performance while fairness analysis focuses on the individual performance each user receives. Until recently, only few grids and cluster systems provided preemptive migration (e.g. [2]), which is the ability of dynamically moving computational tasks across machines during runtime. The emergent technology of virtualization (e.g. [4]) provides o-the-shelf support for migration, thus making the use of this feature more accessible (even across dierent OS’s). In this paper, we study the close relation between migration and fairness. We present fairness and quality of service properties for economic online scheduling algorithms. Under mild assumptions we show that it is impossible to achieve these properties without the use of migration. On the other hand, if zero cost migration is used, then these properties can be satised.

[1]  Dror G. Feitelson,et al.  Utilization, Predictability, Workloads, and User Runtime Estimates in Scheduling the IBM SP2 with Backfilling , 2001, IEEE Trans. Parallel Distributed Syst..

[2]  Renato J. O. Figueiredo,et al.  A case for grid computing on virtual machines , 2003, 23rd International Conference on Distributed Computing Systems, 2003. Proceedings..

[3]  Noam Nisan,et al.  Online ascending auctions for gradually expiring items , 2005, SODA '05.

[4]  Amin Saberi,et al.  An approximation algorithm for max-min fair allocation of indivisible goods , 2007, STOC '07.

[5]  Wayne E. Smith.  Various optimizers for single‐stage production , 1956 .

[6]  Rudolf Müller,et al.  Decentralization and Mechanism Design for Online Machine Scheduling , 2006, SWAT.

[7]  Elchanan Mossel,et al.  On approximately fair allocations of indivisible goods , 2004, EC '04.

[8]  Venkatesan Guruswami,et al.  On profit-maximizing envy-free pricing , 2005, SODA '05.

[9]  David E. Culler,et al.  User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

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

[11]  Ahuva Mu'alem,et al.  Setting lower bounds on truthfulness: extended abstract , 2007, SODA.

[12]  Dan Tsafrir,et al.  A Short Survey of Commercial Cluster Batch Schedulers , 2005 .

[13]  B. Hajek,et al.  Optimal allocation of a divisible good to strategic buyers , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[14]  Lior Amar,et al.  An organizational grid of federated MOSIX clusters , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[15]  D. Parkes,et al.  A Decentralized Auction Framework to Promote Efficient Resource Allocation in Open Computational Grids , 2007 .

[16]  Éva Tardos,et al.  Truthful mechanisms for one-parameter agents , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[17]  Dirk Neumann,et al.  Bridging the Adoption Gap - Developing a Roadmap for Trading in Grids , 2008, Electron. Mark..

[18]  Noam Nisan,et al.  Algorithmic Mechanism Design , 2001, Games Econ. Behav..

[19]  Ryan Porter,et al.  Mechanism design for online real-time scheduling , 2004, EC '04.

[20]  Sanjeev Khanna,et al.  Algorithms for minimizing weighted flow time , 2001, STOC '01.

[21]  Eric J. Friedman,et al.  Pricing WiFi at Starbucks: issues in online mechanism design , 2003, EC '03.

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