NDLI: An adaptive strategy for scheduling data-intensive applications in Grid environments

Content Provider	IEEE Xplore Digital Library
Author	Wantao Liu Kettimuthu, R. Bo Li Foster, I.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, The University of Chicago, Chicago, IL USA (Foster, I.) \|\| School of Computer Science and Engineering, Beihang University, Beijing, China (Wantao Liu; Bo Li) \|\| Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL USA (Kettimuthu, R.)
Abstract	Data-intensive applications are becoming increasingly common in Grid environments. These applications require enormous volume of data for the computation. Most conventional meta-scheduling approaches are aimed at computation intensive application and they do not take data requirement of the applications into account, thus leading to poor performance. Efficient scheduling of data-intensive applications in Grid environments is a challenging problem. In addition to process utilization and average turnaround time, it is important to consider the worst-case turnaround time in evaluating the performance of Grid scheduling strategies. In this paper, we propose an adaptive scheduling scheme that takes into account both the computational requirements and the data requirements of the jobs while making scheduling decisions. In our scheme, data transfer is viewed in par with computation and explicitly considered when scheduling. Jobs are dispatched to the sites that are optimal in terms of both data transfer time and computation time. In addition, our scheme overlaps a job's data transfer time with its own queuing time and other jobs' computation time as much as possible. Trace-based simulations show that the proposed scheme can gain significant performance benefits for data-intensive jobs.
Starting Page	642
Ending Page	649
File Size	370061
Page Count	8
File Format	PDF
ISBN	9781424452460
e-ISBN	9781424452477
DOI	10.1109/ICTEL.2010.5478755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-04
Publisher Place	Qatar
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Concurrent computing Adaptive scheduling Processor scheduling File systems Grid computing File servers Large-scale systems Resource management Application software
Content Type	Text
Resource Type	Article

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