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SLA-aware scheduling of map-reduce applications on public clouds
Citation
Zeng, X and Garg, S and Wen, Z and Strazdins, P and Wang, L and Ranjan, R, SLA-aware scheduling of map-reduce applications on public clouds, Proceedings of the 18th International Conference on High Performance Computing & Communications; 14th International Conference on Smart City; 2nd International Conference on Data Science & Systems, 12-14 December 2016, Sydney, pp. 655-662. ISBN 978-1-5090-4297-5 (2016) [Refereed Conference Paper]
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Copyright Statement
Copyright 2016 IEEE
Official URL: http://dx.doi.org/10.1109/HPCC-SmartCity-DSS.2016....
DOI: doi:10.1109/HPCC-SmartCity-DSS.2016.183
Abstract
The recent need of processing BigData has led to
the development of several Map-Reduce applications for efficient
large scale processing. Due to on-demand availability of large
computing resources, Public Clouds have become a natural host
of these Map-Reduce applications. In this case, users need to
decide which resources they need to rent to run their Map-
Reduce cluster other than deployment or scheduling of map-reduce
tasks itself. This is not a trivial task particularly when
users may have performance constraints such as deadline and
have several Cloud product types to choose with intention of not
spending much money. Even though there are several existing
scheduling systems, however most of them are not developed to
manage the scheduling of Map-Reduce applications. That is, they
do not consider things like the number of map and reduce tasks
and slots per VM. This paper proposes a novel greedy scheduling
algorithm (MASA) that considers the users constraints in order to
minimize cost of renting Cloud resources while considering the
user’s budget and deadline constraints. The simulation results
show 25-60% reduction cost in comparison to current methods
by using our proposed algorithm.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | cloud computing, big data, Hadoop, SLA |
| Research Division: | Information and Computing Sciences |
| Research Group: | Distributed computing and systems software |
| Research Field: | Distributed systems and algorithms |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Information systems, technologies and services not elsewhere classified |
| UTAS Author: | Garg, S (Dr Saurabh Garg) |
| ID Code: | 111794 |
| Year Published: | 2016 |
| Web of Science® Times Cited: | 2 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2016-10-07 |
| Last Modified: | 2019-06-11 |
| Downloads: | 140 View Download Statistics |
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