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

Availability and Network-Aware MapReduce Task Scheduling over the Internet

International audienceMapReduce offers an ease-of-use programming paradigm for processing large datasets. In our previous work, we have designed a MapReduce framework called BitDew-MapReduce for desktop grid and volunteer computing environment, that allows nonexpert users to run data-intensive MapReduce jobs on top of volunteer resources over the Internet. However, network distance and resource availability have great impact on MapReduce applications running over the Internet. To address this, an availability and network-aware MapReduce framework over the Internet is proposed. Simulation results show that the MapReduce job response time could be decreased by 27.15%, thanks to Naive Bayes Classifier-based availability prediction and landmark-based network estimation

Virtual Cluster Management for Analysis of Geographically Distributed and Immovable Data

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Scenarios exist in the era of Big Data where computational analysis needs to utilize widely distributed and remote compute clusters, especially when the data sources are sensitive or extremely large, and thus unable to move. A large dataset in Malaysia could be ecologically sensitive, for instance, and unable to be moved outside the country boundaries. Controlling an analysis experiment in this virtual cluster setting can be difficult on multiple levels: with setup and control, with managing behavior of the virtual cluster, and with interoperability issues across the compute clusters. Further, datasets can be distributed among clusters, or even across data centers, so that it becomes critical to utilize data locality information to optimize the performance of data-intensive jobs. Finally, datasets are increasingly sensitive and tied to certain administrative boundaries, though once the data has been processed, the aggregated or statistical result can be shared across the boundaries. This dissertation addresses management and control of a widely distributed virtual cluster having sensitive or otherwise immovable data sets through a controller. The Virtual Cluster Controller (VCC) gives control back to the researcher. It creates virtual clusters across multiple cloud platforms. In recognition of sensitive data, it can establish a single network overlay over widely distributed clusters. We define a novel class of data, notably immovable data that we call "pinned data", where the data is treated as a first-class citizen instead of being moved to where needed. We draw from our earlier work with a hierarchical data processing model, Hierarchical MapReduce (HMR), to process geographically distributed data, some of which are pinned data. The applications implemented in HMR use extended MapReduce model where computations are expressed as three functions: Map, Reduce, and GlobalReduce. Further, by facilitating information sharing among resources, applications, and data, the overall performance is improved. Experimental results show that the overhead of VCC is minimum. The HMR outperforms traditional MapReduce model while processing a particular class of applications. The evaluations also show that information sharing between resources and application through the VCC shortens the hierarchical data processing time, as well satisfying the constraints on the pinned data

Towards MapReduce for Desktop Grid Computing

International audienceMapReduce is an emerging programming model for data-intense application proposed by Google, which has attracted a lot of attention recently. MapReduce borrows from functional programming, where programmer defines Map and Reduce tasks executed on large set of distributed data. In this paper we propose an implementation of the MapReduce programming model. We present the architecture of the prototype based on Bit Dew, a middleware for large scale data management on Desktop Grid. We describe the set of features which makes our approach suitable for large scale and loosely connected Internet Desktop Grid: massive fault tolerance, replica management, barriers-free execution, latency-hiding optimisation as well as distributed result checking. We also present performance evaluation of the prototype both against micro-benchmarks and real MapReduce application. The scalability test shows that we achieve linear speedup on the classical Word Count benchmark. Several scenarios involving lagger hosts and host crashes demonstrate that the prototype is able to cope with an experimental context similar to real-world Internet