Modeling throughput sampling size for a cloud-hosted data scheduling and optimization service

Title
Modeling throughput sampling size for a cloud-hosted data scheduling and optimization service
Author(s)
김장영Yildirim, Esma[Yildirim, Esma]Kosar, Tevfik[Kosar, Tevfik]
Keywords
PERFORMANCE
Issue Date
201309
Publisher
ELSEVIER SCIENCE BV
Citation
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE, v.29, no.7, pp.1795 - 1807
Abstract
As big-data processing and analysis dominates the usage of the Cloud systems, the need for Cloud-hosted data scheduling and optimization services increases. One key component for such a service is to provide available bandwidth and achievable throughput estimation capabilities, since all scheduling and optimization decisions would be built on top of this information. The biggest challenge in providing these estimation capabilities is the dynamic decision of what proportion of the actual dataset, when transferred, would give us an accurate estimate of the bandwidth and throughput achieved by transferring the whole data set. That proportion of data is called the sampling size (or the probe size). Although small fixed sample sizes worked well for high-latency low-bandwidth networks in the past, high-bandwidth networks require much larger and more dynamic sample sizes, since an accurate estimation now also depends on how fast the transfer protocol can saturate that fat network link. In this study, we present a model to decide the optimal sampling size based on the data size and estimated capacity of the network. Our results show that the predicted sampling size is very accurate compared to the targeted best sampling size for a certain file transfer in a majority of the cases.(C) 2013 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/29053http://dx.doi.org/10.1016/j.future.2013.01.003
ISSN
0167-739X
Appears in Collections:
기초교육대학 > 교양학부 > Articles
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