37,161 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
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
SMiT: Local System Administration Across Disparate Environments Utilizing the Cloud
System administration can be tedious. Most IT departments maintain several (if not several hundred) computers, each of which requires periodic housecleaning: updating of software, clearing of log files, removing old cache files, etc. Compounding the problem is the computing environment itself. Because of the distributed nature of these computers, system administration time is often consumed in repetitive tasks that should be automated. Although current system administration tools exist, they are often centralized, unscalable, unintuitive, or inflexible. To meet the needs of system administrators and IT professionals, we developed the Script Management Tool (SMiT). SMiT is a web-based tool that permits administration of distributed computers from virtually anywhere via a common web browser. SMiT consists of a cloud-based server running on Google App Engine enabling users to intuitively create, manage, and deploy administration scripts. To support local execution of scripts, SMiT provides an execution engine that runs on the organization’s local machines and communicates with the server to fetch scripts, execute them, and deliver results back to the server. Because of its distributed asynchronous architecture SMiT is scalable to thousands of machines. SMiT is also extensible to a wide variety of system administration tasks via its plugin architecture
Exploring heterogeneity of unreliable machines for p2p backup
P2P architecture is a viable option for enterprise backup. In contrast to
dedicated backup servers, nowadays a standard solution, making backups directly
on organization's workstations should be cheaper (as existing hardware is
used), more efficient (as there is no single bottleneck server) and more
reliable (as the machines are geographically dispersed).
We present the architecture of a p2p backup system that uses pairwise
replication contracts between a data owner and a replicator. In contrast to
standard p2p storage systems using directly a DHT, the contracts allow our
system to optimize replicas' placement depending on a specific optimization
strategy, and so to take advantage of the heterogeneity of the machines and the
network. Such optimization is particularly appealing in the context of backup:
replicas can be geographically dispersed, the load sent over the network can be
minimized, or the optimization goal can be to minimize the backup/restore time.
However, managing the contracts, keeping them consistent and adjusting them in
response to dynamically changing environment is challenging.
We built a scientific prototype and ran the experiments on 150 workstations
in the university's computer laboratories and, separately, on 50 PlanetLab
nodes. We found out that the main factor affecting the quality of the system is
the availability of the machines. Yet, our main conclusion is that it is
possible to build an efficient and reliable backup system on highly unreliable
machines (our computers had just 13% average availability)
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