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 hierarchical architecture for a distributed management of p2p networks and services

Abstract: We propose a management architecture for the P2P model which respects its distributed nature while building a hierarchical structure. This architecture enables the distribution of management functions, avoids an excessive centralization of the manager role and fits the dynamic of the P2P model well. The architecture is evaluated through an implementation in the Pastry framework.

An Incrementally Deployable Network Architecture to Support Both Data-Centric and Host-Centric Services

Abstract: Most Internet traffic is associated with applications where users are interested in the data and not in the source where the data resides. On the other hand, the current Internet architecture is host-centric rather than data-centric, and it is not practical to deploy a pure data-centric architecture overnight. This motivates an incrementally deployable network architecture that can efficiently support both data-centric and host-centric services. In this article we describe such an architecture called DCNA for the Internet. DCNA is based on the inclusion of a shim layer between the application layer and the transport layer along with appropriate interfaces to connect these layers. In addition to being data-centric and incrementally deployable, DCNA can also effectively support mobility and multi-homing.

FORMAware: framework of reflective components for managing architecture adaptation

Abstract: Software engineers use abstraction to better understand, model and reason about the surrounding world. Recently Architecture Description Languages (ADLs) introduced new levels of abstraction with potential use at run-time to support system evolution. In this paper we propose the FORMAware architecture that blends run-time architectural representation with a reflective programming model to address adaptation issues and promote the proximity between design and development. Reflection opens up composition architecture through a replaceable default style manager that permits to execute architecture reconfigurations. This manager enforces the structural integrity of the architecture through a set of style rules that developers may change to meet other architectural strategies. Each reconfiguration runs in the scope of a transaction that we may commit or rollback.

Object-oriented real-time computing for reusable avionics software

Abstract: Reusable product line software architectures and supporting components are the focus of an increasing number of software organizations attempting to reduce software costs. One essential attribute of a product line architecture is that it effectively isolates the logical, or static, aspects of the application from any product specific variations in the physical architecture or execution environment. A primary element of this isolation is hardware and low-level software (e.g., operating system) independence. This paper describes our experiences on developing object-oriented physical architectures for large scale reusable embedded systems, and on various ways that physical architecture attributes can be designed for flexibility without introducing volatility into the application architecture.

A Cluster-Based Data-Centric Model for Network-Aware Task Scheduling in Distributed Systems

Abstract: Big Data processing architectures are now widely recognized as one of the most significant innovations in Computing in the last decade. Their enormous potential in collecting and processing huge volumes of data scattered throughout the Internet is opening the door to a new generation of fully distributed applications that, by leveraging the large amount of resources available on the network will be able to cope with very complex problems achieving performances never seen before. However, the Internet is known to have severe scalability limitations in moving very large quantities of data, and such limitations introduce the challenge of making efficient use of the computing and storage resources available on the network, in order to enable data-intensive applications to be executed effectively in such a complex distributed environment. This implies resource scheduling decisions which drive the execution of task towards the data by taking network load and capacity into consideration to maximize data access performance and reduce queueing and processing delays as possible. Accordingly, this work presents a data-centric meta-scheduling scheme for fully distributed Big Data processing architectures based on clustering techniques whose goal is aggregating tasks around storage repositories and driven by a new concept of "gravitational" attraction between the tasks and their data of interest. This scheme will benefit from heuristic criteria based on network awareness and advance resource reservation in order to suppress long delays in data transfer operations and result into an optimized use of data storage and runtime resources at the expense of a limited (polynomial) computational complexity.