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 Reference Architecture Based on Reflection for Self-Adaptive Software

Abstract: Self-adaptive Software (SaS) presents specific characteristics compared to traditional ones, as it makes possible adaptations to be incorporated at runtime. These adaptations, when manually performed, normally become an onerous, error-prone activity. In this scenario, automated approaches have been proposed to support such adaptations; however, the development of SaS is not a trivial task. In parallel, reference architectures are reusable artifacts that aggregate the knowledge of architectures of software systems in specific domains. They have facilitated the development, standardization, and evolution of systems of those domains. In spite of their relevance, in the SaS domain, reference architectures that could support a more systematic development of SaS are not found yet. Considering this context, the main contribution of this paper is to present a reference architecture based on reflection for SaS, named RA4SaS (Reference Architecture for SaS). Its main purpose is to support the development of SaS that presents adaptations at runtime. To show the viability of this reference architecture, a case study is presented. As result, it has been observed that RA4SaS has presented good perspective to efficiently contribute to the area of SaS.

The many dimensions of scalability

Abstract: The scalability of a computer architecture has more dimensions than are usually recognized. These dimensions include performance economics, physical size, addressing, software independence, communication ability, technology independence, and optionality. All must be considered in the design if an architecture is to be successful over a significant time period. Certain recently-developed system architectures have much better scaling properties than did the previous generations, which are now approaching their fundamental limits. >

Quality-Driven Architectural Patterns for Self-Aware Cloud-Based Software

Abstract: Architecture-based self-adaptation has been recognised as one of the prominent ways to design autonomic systems, where self-manageable architectures tend to achieve the required level of dynamicity and compliance with the continual changing in QoS requirements during run-time. Self-awareness and self-expression have recently emerged as promising architectural concepts in the field of self-adaptive software. Self-aware architecture patterns are envisioned as enabler for self-adaptation, but they tend to provide limited support for the QoS run-time requirements. While the research community has developed in architecture quality management, patterns and tactics, addressing quality attributes in self-aware architectures has not been tackled yet. In this paper, we aim to provide quality-driven architectural patterns for emerging class of architecture enabled by the principles of self-awareness. We report on the feasibility of correlating QoS tactics with self-aware capabilities to better respond to QoS run-time requirements and trade-offs. We describe novel extensions which make the correlation between QoS tactics and self-awareness explicit. We quantitatively evaluate the feasibility, generality and fitness of the proposed approach, as well as its potential applicability to self-aware architectures. Though the proposed extensions can potentially benefit architectures which leverage on self-awareness, we use the case of cloud auto-scaling architecture.

Data-Centric Prioritization in a Cognitive Radio Network: A Quality-of-Service Based Design and Integration

Abstract: The under-/un-utilized radio spectrum is an area of emphasis and potential in an effort to increase utilization. This paper introduces a new matrix into the frequency evaluation/determination methodology. It will reduce the number of decision and network collisions in a cognitive radio network environment. This is achieved via a new paradigm, data centric prioritization (DCP), the exploitation of the unique relationship between the transmitted application data type and a "true best fit" cognitive radio frequency decision in a cognitive radio community cluster. Since application data types during normal wireless device operations vary from user to user and utility to utility, so will the network performance requirements. The network must be able to seamlessly support this diversity and a cognitive radio device is best suited for this task. The concept of associating application-specific design requirements with the network dynamics of the frequency spectrum lends itself to a quality-of-service (QoS) methodology. This paper delves into the usage of a QoS methodology within the cognitive radio cognition cycle.

Domain-Driven Design of Big Data Systems based on a Reference Architecture

Abstract: In general, different application domains may require different big data systems. To enhance the understanding of big data systems and support the architect in designing big data architectures, we propose a domain-driven design approach for deriving application architectures. To this end, we propose a domain engineering approach in which a family feature model, reference architecture, and corresponding design rules are identified. The family feature model is derived based on a domain analysis of big data systems and represents the common and variant features. The reference architecture represents a generic structure for various application architectures of big data systems. Finally, the design rules define reusable design heuristics for designing an application architecture based on the selection of features of the family feature model and the reference architecture. We illustrate our approach for deriving the big data architectures of different well-known big data systems.