eCite Digital Repository

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]


Preview
PDF
2Mb
  

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 userís 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
Research Field:Distributed and Grid Systems
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Application Tools and System Utilities
Author:Garg, S (Dr Saurabh Garg)
ID Code:111794
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2016-10-07
Last Modified:2018-01-18
Downloads:50 View Download Statistics

Repository Staff Only: item control page