200 research outputs found
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Handling of Congestion in Cluster Computing Environment Using Mobile Agent Approach
Computer networks have experienced an explosive growth over the past few years and with that growth have come severe congestion problems. Congestion must be prevented in order to maintain good network performance. In this paper, we proposed a cluster based framework to control congestion over network using mobile agent. The cluster implementation involves the designing of a server which manages the configuring, resetting of cluster. Our framework handles - the generation of application mobile code, its distribution to appropriate client, efficient handling of results, so generated and communicated by a number of client nodes and recording of execution time of application. The client node receives and executes the mobile code that defines the distributed job submitted by server and replies the results back. We have also the analyzed the performance of the developed system emphasizing the tradeoff between communication and computation overhead. The effectiveness of proposed framework is analyzed using JDK 1.5
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
Failure-awareness and dynamic adaptation in data scheduling
Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance
Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy
Data collection for scientific applications is increasing exponentially and
is forecasted to soon reach peta- and exabyte scales. Applications which
process and analyze scientific data must be scalable and focus on execution
performance to keep pace. In the field of radio astronomy, in addition to
increasingly large datasets, tasks such as the identification of transient
radio signals from extrasolar sources are computationally expensive. We present
a scalable approach to radio pulsar detection written in Scala that
parallelizes candidate identification to take advantage of in-memory task
processing using Apache Spark on a YARN distributed system. Furthermore, we
introduce a novel automated multiclass supervised machine learning technique
that we combine with feature selection to reduce the time required for
candidate classification. Experimental testing on a Beowulf cluster with 15
data nodes shows that the parallel implementation of the identification
algorithm offers a speedup of up to 5X that of a similar multithreaded
implementation. Further, we show that the combination of automated multiclass
classification and feature selection speeds up the execution performance of the
RandomForest machine learning algorithm by an average of 54% with less than a
2% average reduction in the algorithm's ability to correctly classify pulsars.
The generalizability of these results is demonstrated by using two real-world
radio astronomy data sets.Comment: In Proceedings of the 47th International Conference on Parallel
Processing (ICPP 2018). ACM, New York, NY, USA, Article 11, 11 page
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