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Fault tolerance via diversity for off-the-shelf products: A study with SQL database servers
If an off-the-shelf software product exhibits poor dependability due to design faults, then software fault tolerance is often the only way available to users and system integrators to alleviate the problem. Thanks to low acquisition costs, even using multiple versions of software in a parallel architecture, which is a scheme formerly reserved for few and highly critical applications, may become viable for many applications. We have studied the potential dependability gains from these solutions for off-the-shelf database servers. We based the study on the bug reports available for four off-the-shelf SQL servers plus later releases of two of them. We found that many of these faults cause systematic noncrash failures, which is a category ignored by most studies and standard implementations of fault tolerance for databases. Our observations suggest that diverse redundancy would be effective for tolerating design faults in this category of products. Only in very few cases would demands that triggered a bug in one server cause failures in another one, and there were no coincident failures in more than two of the servers. Use of different releases of the same product would also tolerate a significant fraction of the faults. We report our results and discuss their implications, the architectural options available for exploiting them, and the difficulties that they may present
A comparative analysis of leading relational database management systems
http://deepblue.lib.umich.edu/bitstream/2027.42/96903/1/MBA_JayaramanS_1996Final.pd
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
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
Scattered Simulteneous and Autonomous Access to Encrypted Database
Put the important data on cloud i.e Cloud provider Storage, It should giving the guarantee of security without loss of data while data in use or not in use. Much of the option are available for providing storage services. We decelop an best architecture which integrates data over the cloud and execute multiple operation simultaneously on Encrypted cloud. We are connecting multiple client those are physically distributed. An another advantage we are eliminating the proxies for best performance .the architecture based on theoretical basis. We are providing the prototype to the different client & Network delay.
DOI: 10.17762/ijritcc2321-8169.15070
Designing and Implementing a Distributed Database for a Small Multi-Outlet Business
Data is a fundamental and necessary element for businesses. During their operations they generate a certain amount of data that they need to capture, store, and later on retrieve when required. Databases provide the means to store and effectively retrieve data. Such a database can help a business improve its services, be more competitive, and ultimately increase its profits. In this paper, the system requirements of a distributed database are researched for a movie rental and sale store that has at least two outlets in different locations besides the main one. This project investigates the different stages of such a database, namely, the planning, analysis, decision, implementation and testing
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
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