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Lookup NU author(s): Professor Raj Ranjan
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© 2016 IEEE. 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 MapReduce cluster other than deployment or scheduling of mapreduce 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.
Author(s): Zeng X, Garg S, Wen Z, Strazdins P, Wang L, Ranjan R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016
Year of Conference: 2017
Pages: 655-662
Online publication date: 26/01/2017
Acceptance date: 02/04/2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0097
DOI: 10.1109/HPCC-SmartCity-DSS.2016.0097
Library holdings: Search Newcastle University Library for this item
ISBN: 9781509042968
