Database-centric architecture

About: Database-centric architecture is a research topic. Over the lifetime, 1799 publications have been published within this topic receiving 48836 citations.

A tool architecture to verify properties of multiagent system at runtime

Abstract: This paper describes an architecture allowing to verify properties of a multiagent system during its execution. This architecture is the basis of our study whose goal is to check at runtime, if agents and more generally multiagent systems satisfy requirements. Considering that a correct system is a system verifying the properties specified by the designer, we are interested in the "property" notion. That is why we give here a definition of "property" and we present an architecture to validate them. The architecture, a multiagent system itself, is based on a set of agents whose goals are to check at runtime the whole system's properties. So after a brief description of the "property" notion, we describe our architecture and the way to check systems.

Harnessing Grid Resources with Data-Centric Task Farms

Abstract: As the size of scientific data sets and the resources required for analysis increase, data locality becomes crucial to the efficient use of large scale distributed systems for scientific and data-intensive applications. In order to support interactive analysis of large quantities of data in many scientific disciplines, we propose a data diffusion approach, in which the resources required for data analysis are acquired dynamically, in response to demand. Acquired resources (compute and storage) can be “cached” for some time, thus allowing more rapid responses to subsequent requests. We define an abstract model for data-centric task farms as a common parallel pattern that drives the independent computational tasks, taking into consideration the data locality in order to optimize the performance of the analysis of large datasets. This approach can provide the benefits of dedicated hardware without the associated high costs. We will validate our abstract model through discrete-event simulations; we expect simulations to show the model is both efficient and scalable given a wide range of simulation parameters. To explore the practical realization of our abstract model, we have developed a Fast and Light-weight tasK executiON framework (Falkon). Falkon provides for dynamic acquisition and release of resources, data management capabilities, and the dispatch of analysis tasks via a data-aware scheduler. We have integrated Falkon into the Swift parallel programming system in order to leverage a large number of applications from various domains (astronomy, astro-physics, medicine, chemistry, economics, etc) which cover a variety of different datasets, workloads, and analysis codes. We believe our data-centric task farm model to generalize to many domains and applications, and could offer application end-to-end performance improvements, higher resource utilization, improved efficiency, and better application scalability.

Data Centric Discovery with a Data-Oriented Architecture

Abstract: Increasingly, scientific discovery is driven by the analysis, manipulation, organization, annotation, sharing, and reuse of high-value scientific data. While great attention has been given to the specifics of analyzing and mining data, we find that there are almost no tools nor systematic infrastructure to facilitate the process of discovery from data. We argue that a more systematic perspective is required, and in particular, propose a data-centric approach in which discovery stands on a foundation of data and data collections, rather than on fleeting transformations and operations. To address the challenges of data-centric discovery, we introduce a Data-Oriented Architecture and contrast it with the prevalent Service-Oriented Architecture. We describe an instance of the Data-Oriented Architecture and describe how it has been used in a variety of use cases.

Dependency Models to Manage Software Architecture

Abstract: This article describes a new approach for managing software architectures. It uses inter-module dependencies to specify and manage the architecture of software applications. The technique, based on a matrix representation, is simple, intuitive, and appears to scale far better than the directed graph representations that are used currently. It enables specification and automatic enforcement of architectural intent such as layering and componentization. The article concludes by showing how this approach can be applied to a real application. We build a dependency model to represent the architecture of Ant, a popular Java build utility. We then examine how Ant’s architecture has evolved over several versions of the software.

Model-Driven Evaluation of Software Architecture Quality Using Model Clone Detection

Abstract: As software architecture methods and tools becomeincreasingly model-driven, evaluating architecture artifacts mustadjust correspondingly. Model-driven evaluation of architecturequality has advantages over traditional evaluation techniques, especially when applied in a model-driven context. One approachwe found successful in performing model-driven analysis involvesusing model clone detection, whereby we detect subsystems thatare similar to example systems that are positive and negativequality indicators. In this paper we present our ideas on applyingmodel clone detection to realize model-driven evaluation ofsoftware architectures, which contain many high-level systemsand interactions. We propose having model-based representations of architectural patterns and styles, and employing model clone detection to identify positive and negative architectural aspects for evaluation, including reliability and security. We provide our insights on how this research can be applied to popular architectural paradigms, relation to previous work, and present discussion points on how it will impact software architecture quality evaluation.