Quality-aware realtime Embedded DataBase (QeDB) is a database for data-intensive real-time applications running on embedded devices. Currently, databases for embedded systems are best effort, providing no guarantees on their timeliness and data freshness. Existing real-time database (RTDB) technology cannot be applied to these embedded databases since it hypothesizes that the main memory of a system is large enough to hold the entire database. This, however, might not be true in data-intensive real-time applications. QeDB uses a novel feedback control scheme to support QoS in such embedded systems without requiring all data to reside in main memory. In particular, our approach is based on simultaneous control of both I/O and CPU resources to guarantee the desired timeliness. Unlike existing work on feedback control of RTDB performance, we implement and evaluate the proposed scheme on a modern embedded device. The experimental results show that our approach supports the desired timeliness of transactions while still maintaining high data freshness compared to baseline approaches.
The evolution of the definition of industry into the Smart factories has provide a big improvements in terms of production efficiency and promoted new ways to implement interfaces between humans and machines. A factory plan, which is achieved by means of a set of missions, implies a set of control missions. In this work is introduced how to achieve these control missions into a smart factory through distributed services. These services are provided by integrating smart resources. Smart resources will rely its execution on the control kernel which provides real-time support and reliability to the execution of control tasks. As a conclusions, we introduce a scalable and reusable hierarchy to perform factory plans based on distributed services.
This paper provides a survey of middleware system for Internet of Things (IoT). IoT is considered as a part of future internet
and ubiquitous computing, and it creates a true ubiquitous or smart environment. The middleware for IoT acts as a bond joining
the heterogeneous domains of applications communicating over heterogeneous interfaces. Comprehensive review of the existing
middleware systems for IoT is provided here to achieve the better understanding of the current gaps and future directions
in this field. Fundamental functional blocks are proposed for this middleware system, and based on that a feature wise classification
is performed on the existing IoT-middleware. Open issues are analyzed and our vision on the research scope in this area is
KeywordsInternet of Things–middleware–semantic model–context-awareness–ubiquitous computing
Quality of service policies in communications is one of the current trends in distributed systems based on middleware technology.
To implement the QoS policies it is necessary to define some common parameters. The aim of the QoS policies is to optimize
the user defined QoS parameters. This article describes how to obtain the common QoS parameters using message queues for the
communications and control components of communication. The paper introduces the “Queue-based Quality of Service Cycle” concept
for each middleware component. The QoS parameters are obtained directly from the queue parameters, and Quality of Service
Policies controls directly the message queues to obtain the user-defined parameters values.
The ADAPTIVE Communication Environment (ACE) is an object-oriented (OO) toolkit that implements fundamental design patterns for communication software. ACE is targeted for developers of high-performance communication services and applications on UNIX and Win32 platforms. ACE simplifies the development of OO network applications and services that utilize interprocess communication, event demultiplexing, explicit dynamic linking, and concurrency. ACE automates system configuration and reconfiguration by dynamically linking services into applications at run-time and executing these services in one or more processes or threads. This paperdescribes the structure and functionality of ACE and illustrates core ACE features using examples from domains like telecommunications, enterprise medical imaging, and WWW services. ACE is freely available and is being used for many commercial projects (such as Ericsson, Bellcore, Siemens, Motorola, Kodak, and McDonnell Douglas), as well as many academic ...
Over the past several years' there has been a considerable amount of research within the field of quality of service (QoS) support for distributed multimedia systems. To date, most of the work has been within the context of individual architectural layers' such as the distributed system platform, operating system, trans7ort subsystem and network. Much less progress has been made in addressing the issue of overall end-to-end support for multimedia communications. In recognition of this, a number of research teams have proposed the development of QoS architectures which incorporate quality of service configurable interfaces and quality of service driven control and management mechanisms across all architectural layers'. This paper examines the state-of-the-art in the development of QoS architectures. The approach taken is to present QoS terminology and a generalised QoS framework for understanding and discussing quality of service in the context of distributed multimedia systems. Following this, we evaluate a number of QoS architectures that have emerged in the literature.