Online job provisioning for large scale science experiments over an optical grid infrastructure

Authors:
Xiang Yu;Chunming Qiao;Dantong Yu
Affiliations:
University at Buffalo, SUNY, Amherst, NY;University at Buffalo, SUNY, Amherst, NY;Brookhaven National Lab, Upton, NY
Venue:
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
Year:
2009

Citing 2
Cited 1

The impact of data replication on job scheduling performance in the Data Grid

Future Generation Computer Systems
Algorithms for Integrated Routing and Scheduling for Aggregating Data from Distributed Resources on a Lambda Grid

IEEE Transactions on Parallel and Distributed Systems

Formal QoS Policy Based Grid Resource Provisioning Framework

Journal of Grid Computing

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Abstract

Many emerging science experiments require that the massive data generated by big instruments be accessible and analyzed by a large number of geographically dispersed users. Such large scale science experiments are enabled by an Optical Grid infrastructure which integrates Grid software with a WDM network. This paper studies the following problem in an Optical Grid environment: given an online job request, how to optimally find a host to execute the job, taking into account the need to stage missing input files stored at other places, with the goal of satisfying the job's QoS requirements, subject to dynamic computing and network resource usage status? We first formulate the optimization problem as a Mixed Integer Linear Programming (MILP). As the MILP solution quickly gets intractable when the network size grows larger, we also propose an adaptive heuristic called AOJP. Our simulation results demonstrate both the effectiveness and the efficiency of AOJP.