Challenges and Security Vulnerabilities to Impact on Database Systems

Abstract A lot of institutions depends at present on the systems database operations and their daily activities, thus providing a means for the maintenance of permanent data can refer to it at any time, as well as to search and to modify the data easily. in this paper to determine the security challenges in the databases, and requirements necessary to address those challenges in the data value to provide security base, and to determine the security rules and know the weaknesses that can be exploited any information in a manner not allowed, when he must protect the database to make sure of the following purposes: confidentiality, integrity and continuity. One of the solutions used in this research to protect databases, and encryption of sensitive data, and adjust the system databases, and updates the database systems and we review some of the ways to address these challenges in the security databases

Automated Storage Layout for Database Systems

Modern storage systems are complex. Simple direct-attached storage devices are giving way to storage systems that are flexible, network-attached, consolidated and virtualized. Today, storage systems have their own administrators, who use specialized tools and expertise to configure and manage storage resources. As a result, database administrators are no longer in direct control of the design and configuration of their database systems' underlying storage resources. This introduces problems because database physical design and storage configuration are closely related tasks, and the separation makes it more difficult to achieve a good end-to-end design. For instance, the performance of a database system depends strongly on the storage layout of database objects, such as tables and indexes, and the separation makes it hard to design a storage layout that is tuned to the I/O workload generated by the database system. In this thesis we address this problem and attempt to close the information gap between database and storage tiers by addressing the problem of predicting the storage (I/O) workload that will be generated by a database management system. Specifically, we show how to translate a database workload description, together with a database physical design, into a characterization of the I/O workload that will result. Such a characterization can directly be used by a storage configuration tool and thus enables effective end-to-end design and configuration spanning both the database and storage tiers. We then introduce our storage layout optimization tool, which leverages such workload characterizations to generate an optimized layout for a given set of database objects. We formulate the layout problem as a non-linear programming (NLP) problem and use the I/O characterization as input to an NLP solver. We have incorporated our I/O estimation technique into the PostgreSQL database management system and our layout optimization technique into a database layout advisor. We present an empirical assessment of the cost of both tools as well as the efficacy and accuracy of their results

Automating Layout of Relational Databases

The choice of database layout, i.e., how database objects such as tables and indexes are assigned to disk drives can significantly impact the I/O performance of the system. Today, DBAs typically rely on fully striping objects across all available disk drives as the basic mechanism for optimizing I/O performance. While full striping maximizes I/O parallelism, when query execution involves co-access of two or more large objects, e.g., a merge join of two tables, the above strategy may be suboptimal due to the increased number of random I/O accesses on each disk drive. In this paper, we propose a framework for automating the choice of database layout for a given database that also takes into account the effects of co-accessed objects in the workload faced by the system. We formulate the above as an optimization problem and present an efficient solution to the problem that judiciously takes into account the trade-off between I/O parallelism and random I/O accesses. Our experiments on Microsoft SQL Server show the superior I/O performance of our techniques compared to the traditional approach of fully striping each database object across all disk drives. 1