D 3 -MapReduce: Towards MapReduce for Distributed and Dynamic Data Sets - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

D 3 -MapReduce: Towards MapReduce for Distributed and Dynamic Data Sets

Résumé

Since its introduction in 2004 by Google, MapRe-duce has become the programming model of choice for processing large data sets. Although MapReduce was originally developed for use by web enterprises in large data-centers, this technique has gained a lot of attention from the scientific community for its applicability in large parallel data analysis (including geographic, high energy physics, genomics, etc.). So far MapReduce has been mostly designed for batch processing of bulk data. The ambition of D 3-MapReduce is to extend the MapReduce programming model and propose efficient implementation of this model to: i) cope with distributed data sets, i.e. that span over multiple distributed infrastructures or stored on network of loosely connected devices; ii) cope with dynamic data sets, i.e. which dynamically change over time or can be either incomplete or partially available. In this paper, we draw the path towards this ambitious goal. Our approach leverages Data Life Cycle as a key concept to provide MapReduce for distributed and dynamic data sets on heterogeneous and distributed infrastructures. We first report on our attempts at implementing the MapReduce programming model for Hybrid Distributed Computing Infrastructures (Hybrid DCIs). We present the architecture of the prototype based on BitDew, a middleware for large scale data management, and Active Data, a programming model for data life cycle management. Second, we outline the challenges in term of methodology and present our approaches based on simulation and emulation on the Grid'5000 experimental testbed. We conduct performance evaluations and compare our prototype with Hadoop, the industry reference MapReduce implementation. We present our work in progress on dynamic data sets that has lead us to implement an incremental MapReduce framework. Finally, we discuss our achievements and outline the challenges that remain to be addressed before obtaining a complete D 3-MapReduce environment.
Fichier principal
Vignette du fichier
DataCom_2015.pdf (1.17 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01242046 , version 1 (17-12-2015)

Licence

Paternité - Pas d'utilisation commerciale - Partage selon les Conditions Initiales

Identifiants

  • HAL Id : hal-01242046 , version 1

Citer

Haiwu He, Anthony Simonet, Julio Anjos, José-Francisco Saray, Gilles Fedak, et al.. D 3 -MapReduce: Towards MapReduce for Distributed and Dynamic Data Sets. International Conference on Big Data Intelligence and Computing (DataCom 2015), Dec 2015, Chengdu, China. ⟨hal-01242046⟩
1431 Consultations
267 Téléchargements

Partager

Gmail Facebook X LinkedIn More