Filters








2,003 Hits in 5.8 sec

MapReduce System over Heterogeneous Mobile Devices [chapter]

Peter R. Elespuru, Sagun Shakya, Shivakant Mishra
2009 Lecture Notes in Computer Science  
The proposed MapReduce System over Heterogeneous Mobile Devices consists of three key components: a server component that coordinates and aggregates results, a mobile device client for iPhone, and a traditional  ...  MapReduce systems could see sizable gains of processing throughput by incorporating as many mobile devices as possible in such a heterogeneous environment.  ...  Our solution is The Heterogeneous Mobile Device MapReduce System.  ... 
doi:10.1007/978-3-642-10265-3_16 fatcat:gthd2dh6tjaotmawsgxc3vkbpi

Hadoop MapReduce for Tactical Clouds

Johnu George, Chien-An Chen, Radu Stoleru, Geoffrey G. Xie, Tamim Sookoor, David Bruno
2014 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet)  
By making the MDFS file system work with Hadoop on mobile devices, we hope to enable big data applications on tactical clouds.  ...  The first is Hadoop MapReduce, a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware, and the second is the Mobile Distributed File System  ...  Yet another server-client model based MapReduce system was proposed over a cluster of mobile devices [15] where the mobile client implements MapReduce logic to retrieve work and obtain results from the  ... 
doi:10.1109/cloudnet.2014.6969015 dblp:conf/cloudnet/GeorgeCSXSB14 fatcat:radcs4abgre4lm25z5fy5wbvgu

PER-MARE: Adaptive Deployment of MapReduce over Pervasive Grids

Luiz Angelo Steffenel, Olivier Flauzac, Andrea Schwertner Charao, Patricia Pitthan Barcelos, Benhur Stein, Sergio Nesmachnow, Manuele Kirsch Pinheiro, Daniel Diaz
2013 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing  
MapReduce is a parallel programming paradigm successfully used to perform computations on massive amounts of data, being widely deployed on clusters, grid, and cloud infrastructures.  ...  In this paper we introduce the PER-MARE initiative, which aims at proposing scalable techniques to support existent MapReduce data-intensive applications in the context of loosely coupled networks such  ...  Indeed, mobile devices should be able to come into the environment in a natural way, as their owner moves [17] .  ... 
doi:10.1109/3pgcic.2013.10 dblp:conf/3pgcic/SteffenelFCBSNKD13 fatcat:rofmvzbtozcwboeriflxd6vo4q

Scheduling for real-time mobile MapReduce systems

Adam J. Dou, Vana Kalogeraki, Dimitrios Gunopulos, Taneli Mielikainen, Ville Tuulos
2011 Proceedings of the 5th ACM international conference on Distributed event-based system - DEBS '11  
We present Real-Time Mobile MapReduce (MiscoRT), our system aimed at supporting the execution of distributed applications with real-time response requirements.  ...  The popularity of portable electronics such as smartphones, PDAs and mobile devices and their increasing processing capabilities has enabled the development of several real-time mobile applications that  ...  In the search for economic power, companies are building these services over portable electronics such as smartphones, PDAs and mobile devices.  ... 
doi:10.1145/2002259.2002305 dblp:conf/debs/DouKGMT11 fatcat:najunf7qinhpjmxyow7prb4ukm

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

L. A. Steffenel, O. Flauzac, A. S. Charao, P. P. Barcelos, B. Stein, G. Cassales, S. Nesmachnow, J. Rey, M. Cogorno, M. Kirsch-Pinheiro, C. Souveyet
2014 Journal of Computer Science  
In this study, we propose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including  ...  The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced  ...  Indeed, mobile devices should be able to come into the environment in a natural way, as their owner moves (Coronato and De Pietro, 2008) and devices from different natures, from the desktop and laptop  ... 
doi:10.3844/jcssp.2014.2194.2210 fatcat:7nd7azrvifc6xi6wqrdwp274cu

Hadoop MapReduce for Mobile Clouds

Johnu George, Chien-An Chen, Radu Stoleru, Geoffrey Xie
2016 IEEE Transactions on Cloud Computing  
We have developed the Hadoop MapReduce framework over MDFS and have studied its performance by varying input workloads in a real heterogeneous mobile cluster.  ...  The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing.  ...  In this paper, we implement Hadoop MapReduce framework over MDFS and evaluate its performance on a general heterogeneous cluster of devices.  ... 
doi:10.1109/tcc.2016.2603474 fatcat:2kdyoj2xefeztc3yt2akk5bqo4

A time–energy performance analysis of MapReduce on heterogeneous systems with GPUs

Dumitrel Loghin, Lavanya Ramapantulu, Oana Barbu, Yong Meng Teo
2015 Performance evaluation (Print)  
of MapReduce on heterogeneous systems with GPUs.  ...  Low-power systems, which traditionally target mobile devices market, have made impressive improvements in terms of computational processing and I/O performance.  ...  Moreover, with the performance improvements of wimpy systems traditionally used in mobile devices, we investigate their performance on processing data analytics compared to brawny server systems.  ... 
doi:10.1016/j.peva.2015.06.015 fatcat:h2e3dk2dwjawdlgc4t3g7g3lme

A broadband embedded computing system for MapReduce utilizing Hadoop

YoungHoon Jung, Richard Neill, Luca P. Carloni
2012 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings  
Motivated by these trends, we developed a heterogeneous computing system for MapReduce applications that couples cloud computing with distributed embedded computing.  ...  Specifically, our system combines a central cluster of Linux servers with a broadband network of embedded set-top box (STB) devices.  ...  ACKNOWLEDGMENT This project is partially supported by Cablevision Systems.  ... 
doi:10.1109/cloudcom.2012.6427483 dblp:conf/cloudcom/JungNC12 fatcat:ktzacoxpkjhqlbws2m642rlpbq

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 relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed.  ...  Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues,  ...  The velocity of data generation and growth is increasing because of the proliferation of mobile devices and other device sensors connected to the Internet.  ... 
doi:10.1016/j.is.2014.07.006 fatcat:7ponsae4z5adtowtjkphuqfzjm

BIG DATA ANALYSIS USING HADOOP MAP REDUCE

Brajesh Mishra
2020 International Research Journal of Computer Science  
This paper introduces about big data and Hadoop, HDFS Architecture, MapReduce and applications of Hadoop and the conclusion.  ...  effective storage efficiency, powerful data recovery solutions, scaling and easy for programmers and non-programmers to work with it. effective, and also it can create a duplicate copy of data in case of system  ...  As a rule, this declines the measure of information expected to move over the system.  ... 
doi:10.26562/irjcs.2020.v0705.005 fatcat:xfdlutntjzewpk6tw4qjhzkqpq

Agent-based MapReduce Processing in IoT

Ichiro Satoh
2016 Proceedings of the 8th International Conference on Agents and Artificial Intelligence  
Agent-based MapReduce Processing in IoT.  ...  The framework is based on MapReduce processing, where the MapReduce processing and its clones are popular but inherently have been designed for high-performance server clusters.  ...  Elespuro et al. developed a system for executing MapReduce using heterogeneous devices, e.g., smartphones, from a mobile device client for iPhone (Elespuru et al., 2009 ).  ... 
doi:10.5220/0005802102500257 dblp:conf/icaart/Satoh16 fatcat:n4lsw2wbgrb3jbebnmxwlqn37a

A STRUCTURE FOR HADOOP-COMPATIBLE PRIVATE DATA EVALUATION AND USE OF A BROAD ARRAY OF CLOUD IMAGE PROCESSORS FROM HADOOP ENVIRONMENT

Sudhanshu Gupta, Anjan K Koundinya
2020 International Journal of Engineering Applied Sciences and Technology  
New centuries of devices, images and personal information have strong energy and space but are behind in aspects of Big Data Storage and Cloud Processing software systems.  ...  So far, over 200 billion images have been published on Facebook alone. The average number of images per client is Approximately 200.  ...  In fact, media transcoding methods are essential in order to deliver heterogeneous mobile devices in various sizes to transmit social media information to end-users.  ... 
doi:10.33564/ijeast.2020.v04i12.115 fatcat:ndek6wjp7fcf7dru22f42bdccq

Aura: An IoT Based Cloud Infrastructure for Localized Mobile Computation Outsourcing

Ragib Hasan, Md. Mahmud Hossain, Rasib Khan
2015 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering  
To demonstrate the feasibility of Aura, we have ported a lightweight version of MapReduce to run on IoT devices, and evaluated its performance.  ...  With billions of such devices slated to be deployed in the next five years, we have the opportunity to utilize these devices in converting our physical environment into interactive, smart, and intelligent  ...  The Condor system is a large scale distributed computing platform which runs over a heterogeneous set of servers [5] . mClouds is a mobile device based ad hoc cloud where mobile phones can form a cloud  ... 
doi:10.1109/mobilecloud.2015.37 dblp:conf/mobilecloud/HasanHK15 fatcat:tl5voxcyf5g7xazuxg3faz5m5e

LESS: Big data sketching and Encryption on low power platform

Amey Kulkarni, Colin Shea, Houman Homayoun, Tinoosh Mohsenin
2017 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017  
Ever-growing IoT demands big data processing and cognitive computing on mobile and battery operated devices.  ...  The heterogeneous LESS framework requires 2× less transfer time and achieves 2.25× higher throughput per watt compared to MapReduce platform.  ...  On the other hand, IoT and mobile devices gather data from variety of heterogeneous sources and must provide secured path for data collection and processing.  ... 
doi:10.23919/date.2017.7927253 dblp:conf/date/KulkarniSHM17 fatcat:6jvac25aajfwdhohjmb5siesfa

Adaptive Task Partitioning for Performance Evaluation in Cluster based Heterogeneous Environments

Gosula Anitha
2021 Revista GEINTEC  
The suggested framework Adaptive Control Self-tuning provides a significant improvement over existing methods at moderate to high system utilizations, according to the evaluation results. designs homogeneous  ...  Task scheduling and loaden balancing heterogeneity have been made aware to increase MapReduce performance in heterogeneous situations.  ...  The disparity in MapReduce node handling capabilities can interrupt the assumption that MapReduce A heterogeneous CPU topology system is a system that uses the same ISA, but the core itself differs in  ... 
doi:10.47059/revistageintec.v11i4.2351 fatcat:dd2xruaz4bg27mpt6gy33htbce
« Previous Showing results 1 — 15 out of 2,003 results