Dynamic Provision of Computing Resources from Grid Infrastructures and Cloud Providers

Grid computing involves the ability to harness together the power of computing resources. In this paper we push forward this philosophy and show technologies enabling federation of grid infrastructures regardless of their interface. The aim is to provide the ability to build arbitrary complex grid infrastructure able to sustain the demand required by any given service. In this very same line, this paper also addresses mechanisms that potentially can be used to meet a given quality of service or satisfy peak demands this service may have. These mechanisms imply the elastic growth of the grid infrastructure making use of cloud providers, regardless of whether they are commercial, like Amazon EC2 and GoGrid, or scientific, like Globus Nimbus. Both these technologies of federation and dynamic provisioning are demonstrated in two experiments. The first is designed to show the feasibility of the federation solution by harnessing resources of the TeraGrid, EGEE and Open Science Grid infrastructures through a single point of entry. The second experiment is aimed to show the overheads caused in the process of offloading jobs to resources created in the cloud.

[1]  Eduardo Huedo,et al.  Dynamic Objective and Advance Scheduling in Federated Grids , 2008, OTM Conferences.

[2]  Borja Sotomayor,et al.  Virtual Clusters for Grid Communities , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[3]  José Luis Vázquez-Poletti,et al.  Coordinated harnessing of the IRISGrid and EGEE testbeds with GridWay , 2006, J. Parallel Distributed Comput..

[4]  Rajkumar Buyya,et al.  InterGrid: a case for internetworking islands of Grids , 2008, Concurr. Comput. Pract. Exp..

[5]  Eduardo Huedo,et al.  Dynamic Provisioning of Virtual Clusters for Grid Computing , 2008, Euro-Par Workshops.

[6]  David E. Irwin,et al.  Dynamic virtual clusters in a grid site manager , 2003, High Performance Distributed Computing, 2003. Proceedings. 12th IEEE International Symposium on.

[7]  Shaowen Wang,et al.  Interoperation of world‐wide production e‐Science infrastructures , 2009, Concurr. Comput. Pract. Exp..

[8]  Eduardo Huedo,et al.  Management of Virtual Machines on Globus Grids Using GridWay , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[9]  Eli M. Dow,et al.  Xen and the Art of Repeated Research , 2004, USENIX Annual Technical Conference, FREENIX Track.

[10]  Gregor von Laszewski,et al.  A UNICORE Globus Interoperability Layer , 2002, Comput. Artif. Intell..

[11]  David Abramson,et al.  Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm , 2005, Softw. Pract. Exp..

[12]  Katarzyna Keahey,et al.  Flying Low: Simple Leases with Workspace Pilot , 2008, Euro-Par.

[13]  Eduardo Huedo,et al.  A modular meta-scheduling architecture for interfacing with pre-WS and WS Grid resource management services , 2007, Future Gener. Comput. Syst..

[14]  Edward Walker,et al.  Creating personal adaptive clusters for managing scientific jobs in a distributed computing environment , 2006, 2006 IEEE Challenges of Large Applications in Distributed Environments.

[15]  Liana L. Fong,et al.  Enabling Interoperability among Meta-Schedulers , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[16]  Rajkumar Buyya,et al.  InterGrid: a case for internetworking islands of Grids , 2008 .

[17]  Yong Zhao,et al.  Falkon: a Fast and Light-weight tasK executiON framework , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).