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Enabling Big Data Analytics in the Hybrid Cloud Using Iterative MapReduce

Francisco J. Clemente-Castello, Bogdan Nicolae, Kostas Katrinis, M. Mustafa Rafique, Rafael Mayo, Juan Carlos Fernandez, Daniela Loreti
2015 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)  
While state of art techniques that address workload performance prediction and efficient workload execution over hybrid cloud setups exist, how to address data-intensive workloads -including Big Data Analytics  ...  This paper addresses this gap by taking on the challenge of bursting over hybrid clouds for the benefit of accelerating iterative MapReduce applications.  ...  We focus on one specific class of big data analytics applications that is particularly suitable for "hybrid cloud big data analytics": iterative applications that reuse invariant input data.  ... 
doi:10.1109/ucc.2015.47 dblp:conf/ucc/Clemente-Castello15 fatcat:wmo6c3utunbmbdajulzo5cbtom

Evaluation of Data Locality Strategies for Hybrid Cloud Bursting of Iterative MapReduce

Francisco J. Clemente-Castello, Bogdan Nicolae, M. Mustafa Rafique, Rafael Mayo, Juan Carlos Fernandez
2017 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)  
of big data analytics.  ...  We show that using the right combination of techniques, iterative MapReduce applications can scale well in a hybrid cloud bursting scenario and come even close to the scalability observed in single sites  ...  ACKNOWLEDGMENTS This work was supported by the MINECO/CICYT projects TIN2011-23283, TIN2014-53495-R and FEDER.  ... 
doi:10.1109/ccgrid.2017.96 dblp:conf/ccgrid/Clemente-Castello17 fatcat:julzxysp75hwvmpizgs5ezjaqe

On exploiting data locality for iterative mapreduce applications in hybrid clouds

Francisco J. Clemente-Castelló, Bogdan Nicolae, Rafael Mayo, Juan Carlos Fernández, M. Mustafa Rafique
2016 Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '16  
Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the capacity during peak utilization), has made significant impact especially for big data analytics, where the explosion  ...  Specifically, we focus our study on iterative MapReduce applications, which are a class of large-scale data intensive applications particularly popular on hybrid clouds.  ...  Acknowledgments This work was supported by the MINECO/CICYT projects TIN2011-23283, TIN2014-53495-R and FEDER.  ... 
doi:10.1145/3006299.3006329 dblp:conf/bdc/Clemente-Castello16 fatcat:3xlkkjny6zajtk54p5j2rqfsbu

Performance Model of MapReduce Iterative Applications for Hybrid Cloud Bursting

Francisco J. Clemente-Castello, Bogdan Nicolae, Rafael Mayo, Juan Carlos Fernandez
2018 IEEE Transactions on Parallel and Distributed Systems  
This enables high estimation accuracy, as demonstrated by extensive experiments at scale using a mix of real-life iterative MapReduce applications from standard big data benchmarking suites that cover  ...  data analytics, especially for iterative applications.  ...  However, enabling cloud bursting for big data analytics at large scale poses a major challenge: unlike conventional datacenters where big data analytics applications and middleware run on top of physically  ... 
doi:10.1109/tpds.2018.2802932 fatcat:fjssmetdcjb3bem6jkqkq4owuy

Enabling Strategies for Big Data Analytics in Hybrid Infrastructures

Julio C. S. Anjos, Kassiano J. Matteussi, Paulo R. R. De Souza, Alexandre da Silva Veith, Gilles Fedak, Jorge Luis Victoria Barbosa, Claudio R. Geyer
2018 2018 International Conference on High Performance Computing & Simulation (HPCS)  
This work proposes the use of hybrid infrastructures such as Cloud and Volunteer Computing for Big Data processing and analysis.  ...  In addition, it provides a data distribution model that improves the resource management of Big Data applications in hybrid infrastructures.  ...  The experiments in this paper were carried out using the Grid'5000 experimental testbed, ans conducted under the INRIA ALADDIN development scheme with the support of CNRS, RENATER and several Universities  ... 
doi:10.1109/hpcs.2018.00140 dblp:conf/ieeehpcs/AnjosMSVFBG18 fatcat:dycbqxcqdbfgtcl7jllj4wzeyi

Compiling machine learning algorithms with SystemML

M. Boehm, D. Burdick, A. Evfimievski, B. Reinwald, P. Sen, S. Tatikonda, Y. Tian
2013 Proceedings of the 4th annual Symposium on Cloud Computing - SOCC '13  
Analytics on big data may range from passenger volume prediction in transportation to customer satisfaction in automotive diagnostic systems, and from correlation analysis in social media data to log analysis  ...  SystemML generates hybrid runtime execution plans that range from in-memory, single node execution to large-scale cluster execution of operators, hence enabling scaling up and down of computation.  ...  Implementing algorithms using the declarative language may also have a standardization effect in an enterprise which simplifies reuse and maintenance of analytic algorithms.  ... 
doi:10.1145/2523616.2525965 dblp:conf/cloud/BoehmBERSTT13 fatcat:7uokmzc54rdc7iexuda2nkrmfm

A Combined Analytical Modeling Machine Learning Approach for Performance Prediction of MapReduce Jobs in Cloud Environment

Ehsan Ataie, Eugenio Gianniti, Danilo Ardagna, Ali Movaghar
2016 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)  
The experimental results show how the proposed approach attains a 21% improvement in accuracy over applying machine learning techniques without any support from analytical models.  ...  At the expense of a somewhat reduced performance in comparison to HPC technologies, the MapReduce framework provides fault tolerance and automatic parallelization without any efforts by developers.  ...  of MapReduce jobs in a cloud cluster.  ... 
doi:10.1109/synasc.2016.072 dblp:conf/synasc/AtaieGAM16 fatcat:udouh7sgmjadzj57jf6p7j2xii

Big Data computing and clouds: Trends and future directions

Marcos D. Assunção, Rodrigo N. Calheiros, Silvia Bianchi, Marco A.S. Netto, Rajkumar Buyya
2015 Journal of Parallel and Distributed Computing  
Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions  ...  This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications.  ...  Cloud-enabled Big Data analytics poses several challenges in respect to replicability of analyses.  ... 
doi:10.1016/j.jpdc.2014.08.003 fatcat:l4d5t2y4hrhg5irbyk7bd6zfo4

Research in Big Data and Analytics: An Overview

Lekha R.Nair, Sujala D. Shetty
2014 International Journal of Computer Applications  
This paper presents a brief overview of research progress in various areas associated to Big Data Processing and Analytics and conclude with a discussion on research directions in the same area.  ...  Traditional data analytic methods stumble in dealing with the wide variety of data that comes in huge volumes in a short period of time, demanding a paradigm shift in storage, processing and analysis of  ...  Spark streaming is also popularly used in handling streaming data Big Data Transportation Though big data analytics can be effectively performed in the cloud environment, transfer of the massive data  ... 
doi:10.5120/18980-0407 fatcat:qaixebzdyfhsld6qzqrsk3dd7i

Report on the second international workshop on cloud intelligence (Cloud-I 2013)

Jérôme Darmont, Torben Bach Pedersen
2014 SIGMOD record  
This is even more the case than in cloud computing in general, since cloud intelligence use cases tend to involve very expensive "deep analytics" computations.  ...  Aster-ixDB is a so-called "BDMS (Big Data Management System)" grounded in both parallel database systems and data-intensive computing frameworks (i.e., MapReduce and NoSQL-like frameworks) [2] .  ... 
doi:10.1145/2627692.2627707 fatcat:4r7cqwu3izfbnkgdtxi5uvttii

SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments

Julio C.S. dos Anjos, Marcos D. Assuncao, Jean Bez, Claudio Geyer, Edison Pignaton de Freitas, Alexandre Carissimi, Joao Paulo C. L. Costa, Gilles Fedak, Felix Freitag, Volker Markl, Paul Fergus, Rubem Pereira
2015 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing  
Big data processing techniques are evolving to address this challenge, with analysis increasingly being performed using cloud-based systems.  ...  Observing this landscape in emerging computing system development, this work presents Small & Medium-sized Enterprise Data Analytic in Real Time (SMART) for addressing some of the issues in providing compute  ...  This experiment is a largescale simulation that enables to evaluate the proposal by simulating of algorithms and environment used by SMART, in a hybrid-cloud version of interest.  ... 
doi:10.1109/cit/iucc/dasc/picom.2015.29 dblp:conf/IEEEcit/AnjosABGFCCFFMF15 fatcat:pp3t6xrdzzb6df5fwalnyglwoe

Data Processing Model to Perform Big Data Analytics in Hybrid Infrastructures

Julio C. S. Anjos, Kassiano J. Matteussi, Paulo R. R. De Souza, Gabriel J. A. Grabher, Guilherme A. Borges, Jorge L. V. Barbosa, Gabriel V. Gonzalez, Valderi R. Q. Leithardt, Claudio F. R. Geyer
2020 IEEE Access  
Cloud for Big Data Processing (HCBDP) for performing Big Data analytics.  ...  RELATED WORK This section presents the shortcomings in Big Data analytics in homogeneous, heterogeneous and hybrid environments such as Multi-Cloud, Hybrid Cloud (HC) and VC environments.  ... 
doi:10.1109/access.2020.3023344 fatcat:dmqifexpivhnld75k3uwbmyaui

Data Science and Distributed Intelligence: Recent Developments and Future Insights [chapter]

Alfredo Cuzzocrea, Mohamed Medhat Gaber
2013 Studies in Computational Intelligence  
Big Data, Data Science and MapReduce are three keywords that have flooded our research papers and technical articles during the last two years.  ...  Following this major trend, in this paper we provide a background of these new terms, followed by a discussion of recent developments in the data mining and data warehousing areas in the light of aforementioned  ...  Papadimitriou et al [26] have classified the applicability of speedingup Data Mining algorithms using MapReduce into the following three categories: one-iteration techniques that are perfect for MapReduce  ... 
doi:10.1007/978-3-642-32524-3_18 fatcat:sm3yofpcqbhs5lk2wa3k4xcmgy

The rise of "big data" on cloud computing: Review and open research issues

Ibrahim Abaker Targio Hashem, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, Abdullah Gani, Samee Ullah Khan
2015 Information Systems  
The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced.  ...  Massive growth in the scale of data or big data generated through cloud computing has been observed.  ...  intrusion detection system [48] "MERRA Analytic Services: Meeting the Big Data Challenges of Climate4 Science through Cloud-enabled Climate Analytics-as-a-Service" To address big data challenges in  ... 
doi:10.1016/ fatcat:7ponsae4z5adtowtjkphuqfzjm

BIGhybrid -- A Toolkit for Simulating MapReduce in Hybrid Infrastructures

Julio C.S. dos Anjos, Gilles Fedak, Claudio F.R. Geyer
2014 2014 International Symposium on Computer Architecture and High Performance Computing Workshop  
Merging cloud computing and desktop grids into a hybrid infrastructure can provide a feasible low-cost solution for big data analysis.  ...  This study introduces BIGhybrid, a toolkit that is used to simulate MapReduce in hybrid environments.  ...  The experiments discussed in this paper were conducted with the aid of the Grid'5000 experimental testbed, under the INRIA ALADDIN development plan with support from CNRS, RENATER and a number of universities  ... 
doi:10.1109/sbac-padw.2014.8 dblp:conf/sbac-pad/AnjosFG14 fatcat:2iz4zeicovatzfnz6fc3y64rpa
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