Context Grouping Mechanism for Context Distribution in Ubiquitous Environments

Kirsch-Pinheiro, Manuele; Vanrompay, Yves; Victor, Koen; Berbers, Yolande; Valla, Massimo; Fra, Cristina; Mamelli, Alessandro; Barone, Paolo; Hu, Xiaoming; Devlić, Alisa; Panagiotou, Georgios

doi:10.1007/978-3-540-88871-0_41

Cited by 14 publications

(15 citation statements)

References 12 publications

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“…We believe that this twofold approach is essential to understand and cover all the problem issues. In the context of the PER-MARE project, this paper focus on improving Hadoop fault-tolerance and context-awareness [23], enabling its deployment over a pervasive environment [24]. Figure 5.…”

Section: B the Per-mare Projectmentioning

confidence: 99%

Efficient Prototyping of Fault Tolerant Map-Reduce Applications with Docker-Hadoop

Rey¹,

Cogorno²,

Nesmachnow³

et al. 2015

2015 IEEE International Conference on Cloud Engineering

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Prototyping and testing distributed systems is considered to be a hard task because it is not always possible to reproduce a given sequence of events. While simulations may help on this task, they cannot replace test and validation with real systems. In this paper we present Docker-Hadoop, a container-based virtualization platform designed to prototype, test and deploy MapReduce applications and systems. This tool allowed us to test and reproduce fault-tolerance scenarios that are especially interesting in the context of the PER-MARE project, which aims at adapting the Hadoop framework to the case pervasive systems. Indeed, we developed a fault-tolerant component that can circumvent the limitations from original Hadoop and prevent the job scheduling stall in the case of failures or network disconnections. Thanks to Docker-Hadoop, we could easily prototype and test our improved Hadoop, with the first scalability and speedup results being presented in this paper.

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Section: B the Per-mare Projectmentioning

confidence: 99%

Efficient Prototyping of Fault Tolerant Map-Reduce Applications with Docker-Hadoop

Rey¹,

Cogorno²,

Nesmachnow³

et al. 2015

2015 IEEE International Conference on Cloud Engineering

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“…), the diffusion and analysis of such information must be tightly integrated into the MapReduce framework to boost the platform efficiency. For this reason, context-awareness (Preuveneers et al, 2009) and context distribution (Kirsch-Pinheiro et al 2008) are important elements to be considered.…”

Section: Context-awareness On Hadoopmentioning

confidence: 99%

Mapreduce Challenges on Pervasive Grids

Steffenel¹,

Flauzac²,

Charão³

et al. 2014

Journal of Computer Science

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This study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. 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 context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. 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 fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids.

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“…On the other hand, in decentralized approaches we can find solutions like COR-TEX [29] and MUSIC [14,17]. CORTEX defines sentient objects as autonomous entities that have the capacity of retrieving, processing, and sharing context information using HTTP and SOAP.…”

Section: Context Disseminationmentioning

confidence: 99%

RESTful Integration of Heterogeneous Devices in Pervasive Environments

Romero

Hermosillo

Taherkordi

et al. 2010

Distributed Applications and Interoperable Systems

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Abstract. More and more home devices are equipped with advanced computational capabilities to improve the user satisfaction (e.g., programmable heating system, Internet TV). Although these devices exhibit communication capabilities, their integration into a larger home monitoring system remains a challenging task, partly due to the strong heterogeneity of technologies and protocols. In this paper, we therefore propose to reconsider the architecture of home monitoring systems by focusing on data and events that are produced and triggered by home devices. In particular, our middleware platform, named DIGIHOME, applies i) the REST (REpresentational State Transfer) architectural style to leverage on the integration of multi-scale systems-of-systems (from Wireless Sensor Networks to the Internet) and ii) a CEP (Complex Event Processing) engine to collect information from heterogeneous sources and detect application-specific situations. The benefits of the DIGIHOME platform are demonstrated on smart home scenarios covering home automation, emergency detection, and energy saving situations.

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Context Grouping Mechanism for Context Distribution in Ubiquitous Environments

Cited by 14 publications

References 12 publications

Efficient Prototyping of Fault Tolerant Map-Reduce Applications with Docker-Hadoop

Efficient Prototyping of Fault Tolerant Map-Reduce Applications with Docker-Hadoop

Mapreduce Challenges on Pervasive Grids

RESTful Integration of Heterogeneous Devices in Pervasive Environments

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