109,608 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
Deep Space Network information system architecture study
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control
Efficient HTTP based I/O on very large datasets for high performance computing with the libdavix library
Remote data access for data analysis in high performance computing is
commonly done with specialized data access protocols and storage systems. These
protocols are highly optimized for high throughput on very large datasets,
multi-streams, high availability, low latency and efficient parallel I/O. The
purpose of this paper is to describe how we have adapted a generic protocol,
the Hyper Text Transport Protocol (HTTP) to make it a competitive alternative
for high performance I/O and data analysis applications in a global computing
grid: the Worldwide LHC Computing Grid. In this work, we first analyze the
design differences between the HTTP protocol and the most common high
performance I/O protocols, pointing out the main performance weaknesses of
HTTP. Then, we describe in detail how we solved these issues. Our solutions
have been implemented in a toolkit called davix, available through several
recent Linux distributions. Finally, we describe the results of our benchmarks
where we compare the performance of davix against a HPC specific protocol for a
data analysis use case.Comment: Presented at: Very large Data Bases (VLDB) 2014, Hangzho
An Experiment on Bare-Metal BigData Provisioning
Many BigData customers use on-demand platforms in the cloud, where they can get a dedicated virtual cluster in a couple of minutes and pay only for the time they use. Increasingly, there is a demand for bare-metal bigdata solutions for applications that cannot tolerate the unpredictability and performance degradation of virtualized systems. Existing bare-metal solutions can introduce delays of 10s of minutes to provision a cluster by installing operating systems and applications on the local disks of servers. This has motivated recent research developing sophisticated mechanisms to optimize this installation. These approaches assume that using network mounted boot disks incur unacceptable run-time overhead. Our analysis suggest that while this assumption is true for application data, it is incorrect for operating systems and applications, and network mounting the boot disk and applications result in negligible run-time impact while leading to faster provisioning time.This research was supported in part by the MassTech
Collaborative Research Matching Grant Program, NSF
awards 1347525 and 1414119 and several commercial
partners of the Massachusetts Open Cloud who may be
found at http://www.massopencloud.or
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