An Advanced MapReduce: Cloud MapReduce, Enhancements and Applications

Dahiphale, Devendra; Karve, Rutvik; Vasilakos, Athanasios V.; Liu, Huan; Yu, Zhonghua; Chhajer, Amit; Wang, Jianmin; Wang, Chaokun

doi:10.1109/tnsm.2014.031714.130407

Cited by 47 publications

(35 citation statements)

References 13 publications

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“…D Dahiphale et al [32] described the issues of the conventional MapReduce frameworks as a) The MapReduce framework uses a sequential processing of the Map and Reduce stages. b) The scalability of the Map Reduce is limited.…”

Section: Literature Surveymentioning

confidence: 99%

“…To overcome these drawbacks a pipelined model is adopted to parallelize the execution of the map and reduce phase. The MapReduce model proposed in [32] is realized on the Amazon public EC2 cloud. The spot instance offering of the Amazon cloud enables flexible pricing.…”

Section: Literature Surveymentioning

confidence: 99%

“…The experimental study presented considering the word count application proves the efficiency of the pipelining based MapReduce model when compared to the conventional MapReduce model. The major drawback of the model proposed in [32] is that the locality optimization is not considered and hosting of additional data dependent applications like Smith Waterman etc. cannot be executed in a pipelined fashion.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performance of Hadoop suffers when multiway joins are considered [22]. The Hadoop framework considers sequential processing of the map and then the reduce stages that effects performance [23].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Bulk Synchronous Parallelized Scheduler for Bioinformatics Application on Public Cloud

Algur¹,

Sakri²

2016

IJCA

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Genomic sequence alignment of varied species is one of the most sort of applications in bioinformatics. In future bioinformatics technologies are expected to produce genomic data of terabyte. Bioinformatics computation require super computer for sequence alignment computation which involves huge cost. Parallelization technique is a way forward in computing sequence alignment with limited cost and time. Cloud computing and MapReduce framework play an important role in bioinformatics intensive application to achieve parallelization since it provides a consistent performance over time and it also provides good fault tolerant mechanism. The existing gene sequencing methodologies are designed based on Hadoop-MapReduce framework which adopts a serial execution strategy which is an area of concern. This work introduces a Smith-Waterman Alignment on the Bulk synchronous Parallel Map Reduce (SW-BSPMR) cloud platform for bioinformatics gene sequence alignment. This work adopts a widely accepted and accurate SW algorithm for sequence alignment and parallel synchronous scheduler methodology of map and reduce framework process is considered. A customized MapReduce based on Microsoft Azure cloud platform is developed to overcome the issue in Hadoop-MapReduce framework. The experimental study presented in this work proves that the SW-BSPMR can accurately and effectively align bioinformatics genomic sequences of various read length.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Bulk Synchronous Parallelized Scheduler for Bioinformatics Application on Public Cloud

Algur¹,

Sakri²

2016

IJCA

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“…MapReduce is a popular programming model for dataintensive tasks and has been widely used in many fields [9][10][11][12][13][14]. Hadoop is an opensource implementation of MapReduce, and a Hadoop cluster can be made up of thousands of commodity computers.…”

Section: Introductionmentioning

confidence: 99%

Profit-oriented task scheduling algorithm in Hadoop cluster

Chai

Dong

2016

J Embedded Systems

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Nowadays, many enterprises provide cloud services based on their own Hadoop clusters. Because the resources of a Hadoop cluster are limited, the Hadoop cluster must select some specific tasks to allocate limited resources in order to get the maximal profit. In this paper, we study the maximal profit problem for a given candidate task set. We describe the candidate task set with a valid sequence and propose a sequence-based scheduling strategy. In order to improve the efficiency of finding a valid sequence, we design some pruning strategies and give the corresponding scheduling algorithm. Finally, we propose a timeout handling algorithm when some task runs timeout. Experiments show that the total profit of the proposed algorithm is very close to the ideal maxima and is obviously bigger than related scheduling algorithms under different experimental settings.

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