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

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

Transactions and data management in NoSQL cloud databases

NoSQL databases have become the preferred option for storing and processing data in cloud computing as they are capable of providing high data availability, scalability and efficiency. But in order to achieve these attributes, NoSQL databases make certain trade-offs. First, NoSQL databases cannot guarantee strong consistency of data. They only guarantee a weaker consistency which is based on eventual consistency model. Second, NoSQL databases adopt a simple data model which makes it easy for data to be scaled across multiple nodes. Third, NoSQL databases do not support table joins and referential integrity which by implication, means they cannot implement complex queries. The combination of these factors implies that NoSQL databases cannot support transactions. Motivated by these crucial issues this thesis investigates into the transactions and data management in NoSQL databases. It presents a novel approach that implements transactional support for NoSQL databases in order to ensure stronger data consistency and provide appropriate level of performance. The novelty lies in the design of a Multi-Key transaction model that guarantees the standard properties of transactions in order to ensure stronger consistency and integrity of data. The model is implemented in a novel loosely-coupled architecture that separates the implementation of transactional logic from the underlying data thus ensuring transparency and abstraction in cloud and NoSQL databases. The proposed approach is validated through the development of a prototype system using real MongoDB system. An extended version of the standard Yahoo! Cloud Services Benchmark (YCSB) has been used in order to test and evaluate the proposed approach. Various experiments have been conducted and sets of results have been generated. The results show that the proposed approach meets the research objectives. It maintains stronger consistency of cloud data as well as appropriate level of reliability and performance

Data management in cloud environments: NoSQL and NewSQL data stores

: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages

An automated wrapper-based approach to the design of dependable software

The design of dependable software systems invariably comprises two main activities: (i) the design of dependability mechanisms, and (ii) the location of dependability mechanisms. It has been shown that these activities are intrinsically difficult. In this paper we propose an automated wrapper-based methodology to circumvent the problems associated with the design and location of dependability mechanisms. To achieve this we replicate important variables so that they can be used as part of standard, efficient dependability mechanisms. These well-understood mechanisms are then deployed in all relevant locations. To validate the proposed methodology we apply it to three complex software systems, evaluating the dependability enhancement and execution overhead in each case. The results generated demonstrate that the system failure rate of a wrapped software system can be several orders of magnitude lower than that of an unwrapped equivalent

Single-click to data insights: transaction replication and deployment automation made simple for the cloud age

In this report we present out initial work on making the MonetDB column-store analytical database ready for Cloud deployment. As we stand in the new space between research and industry we have tried to combine approaches from both worlds. We provide details how we utilize modern technologies and tools for automating building of virtual machine image for Cloud, datacentre and desktop use. We also explain our solution to asynchronous transaction replication MonetDB. The report concludes with how this all ties together with our efforts to make MonetDB ready for the age where high-performance data analytics is available in a single-click