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Data management applications deployed on IaaS cloud environments must simultaneously strive to minimize cost and provide good performance. Balancing these two goals requires complex decision-making across a number of axes: resource provisioning, query placement, and query scheduling. While previous works have addressed each axis in isolation for specific types of performance goals, this demonstration...
In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing the human effort of data exploration. Towards...
As elastic IaaS clouds continue to become more cost efficient than on-site datacenters, a wide range of data management applications are migrating to pay-as-you-go cloud computing resources. These diverse applications come with an equally diverse set of performance goals, resource demands, and budget constraints. While existing research has tackled individual tasks such as query placement, scheduling,...
Recent advances in the underlying architectures of database management systems (DBMS) have motivated the redesign of key DBMS components such as the query optimizer. Optimizers are inherently difficult to build and maintain, and yet there exists no software engineering tools to facilitate their development. In this paper, we introduce a [Devel]opment Environment for Query [Op]timizers (Devel-Op) designed...
Interactive Data Exploration (IDE) applications typically involve users that aim to discover interesting objects by it-eratively executing numerous ad-hoc exploration queries. Therefore, IDE can easily become an extremely labor and resource intensive process. To support these applications, we introduce a framework that assists users by automatically navigating them through the data set and allows...
Despite the fast growth and increased adoption of cloud databases, challenges related to Service-Level-Agreements (SLAs) specification and management still exist. Supporting application-specific performance goals and SLAs, assigning incoming query processing workloads to the reserved resources to avoid SLA violations and monitoring performance factors to ensure acceptable QoS levels, are some of the...
Despite the fast growth and increased adoption of cloud databases the lack of application-specific Service-Level-Agreements (SLAs) hinders the adoption of cloud data services by large-scale enterprises. Defining application-specific QoS objectives and constraints, monitoring the performance factors to ensure acceptable QoS levels and isolating the source of QoS degradation, are some of the critical...
We discuss the problem of resource provisioning for database management systems operating on top of an Infrastructure-As-A-Service (IaaS) cloud. To solve this problem, we describe an extensible framework that, given a target query workload, continually optimizes the system's operational cost, estimated based on the IaaS provider's pricing model, while satisfying QoS expectations. Specifically, we...
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