Sensor grid middleware metamodeling and analysis

Sensor grid is a platform that combines wireless sensor networks and grid computing with the aim of exploiting the complementary advantages of the two systems. Proper integration of these distinct systems into effective, logically single platform is challenging. This paper presents an approach for practical sensor grid implementation and management. The proposed approach uses a metamodeling technique and performance analysis and tuning as well as a middleware infrastructure that enable practical sensor grid implementation and management. The paper presents our implementation and analysis of the sensor grid. © 2014 Srimathi Chandrasekaran et al

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

International audienceThis 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 pro-pose 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

BIGhybrid - A Toolkit for Simulating MapReduce on Hybrid Infrastructures

Cloud computing has increasingly been used as a platform for running large business and data processing applications. Although clouds have become highly popular, when it comes to data processing, the cost of usage is not negligible. Conversely, Desktop Grids, have been used by a plethora of projects, taking advantage of the high number of resources provided for free by volunteers. Merging cloud computing and desktop grids into hybrid infrastructure can provide a feasible low-cost solution for big data analysis. Although frameworks like MapReduce have been conceived to exploit commodity hardware, their use on hybrid infrastructure poses some challenges due to large resource heterogeneity and high churn rate. This study introduces BIGhybrid a toolkit to simulate MapReduce on hybrid environments. The main goal is to provide a framework for developers and system designers to address the issues of hybrid MapReduce. In this paper, we describe the framework which simulates the assembly of two existing middleware: BitDew- MapReduce for Desktop Grids and Hadoop-BlobSeer for Cloud Computing. Experimental results included in this work demonstrate the feasibility of our approach