Scalable storage for a DBMS using transparent distribution

Scalable Distributed Data Structures (SDDSs) provide a self-managing and self-organizing data storage of potentially unbounded size. This stands in contrast to common distribution schemas deployed in conventional distributed DBMS. SDDSs, however, have mostly been used in synthetic scenarios to investigate their properties. In this paper we concentrate on the integration of the LH* SDDS into our efficient and extensible DBMS, called Monet. We show that this merge permits processing very large sets of distributed data. In our implementation we extended the relational algebra interpreter in such a way that access to data, whether it is distributed or locally stored, is transparent to the user. The on-the-fly optimization of operations --- heavily used in Monet --- to deploy different strategies and scenarios inside the primary operators associated with an SDDS adds self-adaptiveness to the query system; it dynamically adopts itself to unforeseen situations. We illustrate the performance efficiency by experiments on a network of workstations. The transparent integration of SDDSs opens new perspectives for very large self-managing database systems

Poster session: Constrained dynamic physical database design

Physical design has always been an important part of database administration. Today's commercial database management systems offer physical design tools, which recommend a physical design for a given workload. However, these tools work only with static workloads and ignore the fact that workloads, and physical designs, may change over time. Research has now begun to focus on dynamic physical design, which can account for time-varying workloads. In this paper, we consider a dynamic but constrained approach to physical design. The goal is to recommend dynamic physical designs that reflect major workload trends but that are not tailored too closely to the details of the input workloads. To achieve this, we constrain the number of changes that are permitted in the recommended design. In this paper we present our definition of the constrained dynamic physical design problem and discuss several techniques for solving it

PRICE DEMAND MODEL FOR A CLOUD CACHE

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution

Estimating the compression fraction of an index using sampling

Data compression techniques such as null suppression and dictionary compression are commonly used in today’s database systems. In order to effectively leverage compression, it is necessary to have the ability to efficiently and accurately estimate the size of an index if it were to be compressed. Such an analysis is critical if automated physical design tools are to be extended to handle compression. Several database systems today provide estimators for this problem based on random sampling. While this approach is efficient, there is no previous work that analyses its accuracy. In this paper, we analyse the problem of estimating the compressed size of an index from the point of view of worst-case guarantees. We show that the simple estimator implemented by several database systems has several “good” cases even though the estimator itself is agnostic to the internals of the specific compression algorithm. efficiently. The naïve method of actually building and compressing the index in order to estimate its size, while highly accurate is prohibitively inefficient. Thus, we need to be able to accurately estimate the compressed size of an index without incurring the cost of actually compressing it. This problem is challenging because the size of the compressed index can depend significantly on the data distribution as well as the compression technique used. This is in contrast with the estimation of the size of an uncompressed index in physical database design tools which can be derived in a straightforward manner from the schema (which defines the size of the corresponding column) and the number of rows in the table

DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts

NoSQL Schema Design for Time-Dependent Workloads

In this paper, we propose a schema optimization method for time-dependent workloads for NoSQL databases. In our proposed method, we migrate schema according to changing workloads, and the estimated cost of execution and migration are formulated and minimized as a single integer linear programming problem. Furthermore, we propose a method to reduce the number of optimization candidates by iterating over the time dimension abstraction and optimizing the workload while updating constraints

Class-Based Continuous Query Scheduling in Data Stream Management Systems

The emergence of Data Stream Management Systems (DSMS) facilitates implementing many types of monitoring applications via continuous queries (CQs). However, different monitoring applications will have different quality-of-service (QoS) requirements for detecting events. For example, the CQs for detecting anomalous events (e.g., fire, flood) have stricter response time requirements over CQs which are for logging and keeping statistical information of interesting physical phenomena. Traditional DSMSs treat all the CQs as being equally important in the system and attempt to optimize their overall performance. In particular, they employ a CQ scheduler to decide the execution order of CQs to achieve a global performance goal and as such perform badly in an environment where CQs have different importance levels. The hypothesis of this research is that there is a need for a suite of schedulers that optimizes the response time of important CQs while satisfying the requirements of the other, less important classes and taking into consideration the underlying processing environment. Toward this, we first develop the Continuous Query Class (CQC) scheduler for single-core / single-process systems which is assumed by many of the current DSMS prototypes, including our own, AQSIOS. Then, we propose the Adaptive Broadcast Disks scheduler (ABD) which is more suitable for dual-core environments. After that, we extend our work to multi-core environments to take advantage of modern machine architectures and their processing capabilities. We propose the Multi-core Broadcast Disk scheduler (MBD) which optimizes the response time of the critical CQs while maintaining acceptable performance for less-critical classes. In addition, it also utilizes the cores efficiently and provides better performance. We demonstrate the effectiveness of our schedulers through a thorough experimental evaluation using new metrics under AQSIOS, our prototype DSMS, and SimAQSIOS, a simulator that closely mimics AQSIOS

Database Auto Awesome: Enhancing Database-Centric Web Applications through Informed Code Generation

Database Auto Awesome is an approach to enhancing web applications comprised of forms used to interact with stored information. It was inspired by Google\u27s Auto Awesome tool, which provides automatic enhancements for photos. Database Auto Awesome aims to automatically or semi-automatically provide improvements to an application by expanding the functionality of the application and improving the existing code. This thesis describes a tool that gathers information from the application and provides details on how the parts of the application work together. This information provides the details necessary to generate new portions of an application. These enhancements are directed by the web application administrator through specifying what they would like to have generated, in terms of functionality. Once the administrator has provided this direction, the new application code is generated and put in updated or new files. Using this approach, Database Auto Awesome provides a viable solution for semi-automatically generating enhancements to an existing web application