Group communications and database replication:techniques, issues and performance

Databases are an important part of today's IT infrastructure: both companies and state institutions rely on database systems to store most of their important data. As we are more and more dependent on database systems, securing this key facility is now a priority. Because of this, research on fault-tolerant database systems is of increasing importance. One way to ensure the fault-tolerance of a system is by replicating it. Replication is a natural way to deal with failures: if one copy is not available, we use another one. However implementing consistent replication is not easy. Database replication is hardly a new area of research: the first papers on the subject are more than twenty years old. Yet how to build an efficient, consistent replicated database is still an open research question. Recently, a new approach to solve this problem has been proposed. The idea is to rely on some communication infrastructure called group communications. This infrastructure offers some high-level primitives that can help in the design and the implementation of a replicated database. While promising, this approach to database replication is still in its infancy. This thesis focuses on group communication-based database replication and strives to give an overall understanding of this topic. This thesis has three major contributions. In the structural domain, it introduces a classification of replication techniques. In the qualitative domain, an analysis of fault-tolerance semantics is proposed. Finally, in the quantitative domain, a performance evaluation of group communication-based database replication is presented. The classification gives an overview of the different means to implement database replication. Techniques described in the literature are sorted using this classification. The classification highlights structural similarities of techniques originating from different communities (database community and distributed system community). For each category of the classification, we also analyse the requirements imposed on the database component and group communication primitives that are needed to enforce consistency. Group communication-based database replication implies building a system from two different components: a database system and a group communication system. Fault-tolerance is an end-to-end property: a system built from two components tends to be as fault-tolerant as the weakest component. The analysis of fault-tolerance semantics show what fault-tolerance guarantee is ensured by group communication based replication techniques. Additionally a new faulttolerance guarantee, group-safety, is proposed. Group-safety is better suited to group communication-based database replication. We also show that group-safe replication techniques can offer improved performance. Finally, the performance evaluation offers a quantitative view of group communication based replication techniques. The performance of group communication techniques and classical database replication techniques is compared. The way those different techniques react to different loads is explored. Some optimisation of group communication techniques are also described and their performance benefits evaluated

Binary vote assignment on grid quorum replication technique with association rule

One of the biggest challenges that data grids users have to face today relates to the improvement of the data management. Organizations need to provide current data to users who may be geographically remote and to handle a volume of requests of data distributed around multiple sites in distributed environment. Therefore, the storage, availability, and consistency are important issues to be addressed to allow efficient and safe data access from many different sites. One way to effectively cope with these challenges is to rely on the replication technique. Replication is a useful technique for distributed database systems. Through this technique, a data can be accessed from multiple locations. Thus, replication increases data availability and accessibility to users. When one site fails, user still can access the same data at another site. Techniques such as Read-One-Write-All (ROWA), Hierarchical Replication Scheme (HRS) and Branch Replication Scheme (BRS) are the popular techniques being used for replication and data management. However, these techniques have its weaknesses in terms of communication costs that is the total replication servers needed to replicate the data. Furthermore, these techniques also do not consider the correlation between data during the fragmentation process. The knowledge about data correlation can be extracted from historical data using techniques of the data mining field. Without proper strategies, replication increases job execution time. In this research, the some-data-to-some-sites scheme called Binary Vote Assignment on Grid Quorum with Association (BV AGQAR) is proposed to manage replication for meaningful fragmented data in distributed database environment with low communication cost and processing time for a transaction. The main feature of BV AGQ-AR is that the technique integrates replication and data mining technique allowing meaningful extraction of knowledge from large data sets. Performance of the BVAGQ-AR technique comprised the following steps. First step is mining the data by using Apriori algorithm from Association Rules. It is used to discover the correlation between data. For the second step, the database is fragmented based on the data mining analysis results. This technique is executed to make sure data replication can be effectively done while saving cost. Then, the databases that are resulted after the fragmentation process are allocated at their assigned sites. Finally, after allocation process, each site has a database file and ready for any transaction and replication process. Finally, the result of the experiments shows that BV AGQ-AR can preserve the data consistency with the lowest communication cost and processing time for a transaction as compared to BCSA, PRA, ROW A, HRS and BRS

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

Parallel Deferred Update Replication

Deferred update replication (DUR) is an established approach to implementing highly efficient and available storage. While the throughput of read-only transactions scales linearly with the number of deployed replicas in DUR, the throughput of update transactions experiences limited improvements as replicas are added. This paper presents Parallel Deferred Update Replication (P-DUR), a variation of classical DUR that scales both read-only and update transactions with the number of cores available in a replica. In addition to introducing the new approach, we describe its full implementation and compare its performance to classical DUR and to Berkeley DB, a well-known standalone database