A Survey on Experimental Performance Evaluation of Data Distribution Service (DDS) Implementations

The Data Distribution Service (DDS) is a widely used communication specification for real-time mission-critical systems that follow the principles of publish-subscribe middleware. DDS has an extensive set of quality of service (QoS) parameters allowing a thorough customisation of the intended communication. An extensive survey of the performance of the implementations of this communication middleware is lacking. This paper closes the gap by surveying the state of the art in performance of various DDS implementations and identifying any research gaps that exist within this domain.Comment: 20 pages and 1 figur

Aplicación de la especificación Data Distribution Service (DDS) al control de glucosa en pacientes diabéticos

En este trabajo se ha investigado la posibilidad de utilizar el estándar DDS (Data Distribution Service) desarrollado por el OMG (Object Management Group) para la monitorización en tiempo real del nivel de glucosa en pacientes diabéticos. Dicho estándar sigue el patrón publicador/suscriptor de modo que, en la prueba de concepto desarrollada, los sensores del punto de cuidado son publicadores de los valores de glucosa de los pacientes y diferentes supervisores se suscriben a esa información. Estos supervisores reaccionan de la forma más adecuada a los valores y la evolución del nivel de glucosa en el paciente, por ejemplo, registrando el valor de la muestra o generando una alarma. El software de intermediación que soporta la comunicación de datos sigue el estándar DDS. Esto facilita por un lado la escalabilidad e interoperatividad de la solución desarrollada y por otro la monitorización de niveles de glucosa y la activación de protocolos predefinidos en tiempo real. La investigación se enmarca dentro del proyecto intramural PERSONA del CIBER-BBN, cuyo objetivo es el diseño de herramientas de soporte a la decisión para la monitorización continua de pacientes personalizadas e integradas en una plataforma tecnológica para diabetes

Machine Learning for Performance Prediction of Data Distribution Service (DDS)

Networking middleware following the Data Distribution Service (DDS) specification is used in real-time missioncritical systems such as autonomous vehicles, energy management systems, and air traffic control. DDS follows the publishsubscribe communication pattern and offers a set of Quality of Service (QoS) parameters, allowing the users to align the data communication to the needs of the application. Configuring DDS to achieve the required performance is a difficult task, given the large space of QoS parameter values. Experimental evaluation of performance levels with a real DDS system for different QoS configurations can be complex and require substantial time and resources. We propose the use of Machine Learning (ML) models to predict the performance metric distribution of DDS under different configurations. This is done by using performance measurements of some configurations to train an ML model. The trained model can then be used to predict the performance distribution of DDS under other system configurations. Since the prediction is computationally inexpensive, we can predict the performance of many different configurations to find a suitable one for given requirements. To the best of our knowledge, this is the first time this approach has been applied to DDS performance evaluation. We used random forests (RF) as an ML method and linear regression (LR) as a baseline. We selected thirteen performance metrics, and for each, we trained an RF model and tuned its hyperparameters. We tested the final models on system configurations unseen during training, both for parameter values within the training range (interpolation) and outside the training range (extrapolation). The RF models show better predictive accuracy than the LR baseline. This paper focuses on the models for throughput and latency - the two well-established performance metrics. The models demonstrate coefficients of determination greater than 0.9 and 0.8, respectively, for different unseen system configurations in interpolation, but work less well in extrapolation cases. We conclude that the proposed ML models offer a way of predicting the performance distribution of a range of configurations when interpolation is used. Since model prediction is computationally much cheaper than relying on experimentation, it is a useful tool to guide DDS system parametrisation and design

Высокопродуктивные системы реального времени для крупномасштабных приложений

The article discusses creation of highly productive real-time systems based on data-centric architecture. For performance issues, efficiency and maintainability of such systems it is proposed using technology of data distribution based on the data distribution service (DDS), with time synchronization based on IEEE 1588.Розглянуті питання створення високопродуктивних систем реального часу на базі датацентричної архітектури з використанням технології розподілу даних (DDS), з підтримкою стандарту синхронізації часу IEEE 1588.Рассмотрены вопросы создания высокопродуктивных систем реального времени на базе дата-центрической архитектуры с использованием технологии распределения данных (DDS), с поддержкой стандарта синхронизации времени IEEE 1588

Security and Performance Trade-offs for Data Distribution Service in Flying Ad-Hoc Networks

This paper focuses on the data distribution service(DDS) middleware and its publish/subscribe logic - a topic thathas recently regained popularity in both academia as well asindustry. DDS is a well-known approach based on publish-subscribe logic. Therefore, only brief introduction of the issueis given followed by practical evaluation of current, availableand real implementations from the security and performancepoint of view. The analysis and evaluation is performed toaid comparison of competing DDS implementation, and thuscould serve well as an input to decision-making about whichof these solutions is best suited for a given situation. Finally,the practical performance evaluation is performed via severaldifferent scenarios to effectively compare the currently most-usedDDS implementations

ANALISIS PERBANDINGAN ANTARA PROTOKOL MESSAGE QUEUE TELEMETRY TRANSPORT (MQTT) DAN DATA DISTRIBUTION SERVICE (DDS)

Protokol publish/subscribe termasuk protokol yang popular pada Internet of Things (IoT), lantaran kemampuannya dalam menyebarkan informasi ke banyak device secara kontinu. Secara garis besar, terdapat dua arsitektur yang dapat digunakan untuk protokol ini. Yang satu menggunakan broker dan yang lainnya tidak menggunakan broker. Pada penelitian ini dilakukan perbandingan protokol publish/subscribe yaitu, Message Queue Telemetry Transport (MQTT) dan Data Distribution Service (DDS). Arsitektur jaringan pada MQTT menggunakan broker sebagai perantara pengiriman message, dan arsitektur jaringan pada DDS tidak menggunakan broker. Perbandingan performansi terhadap MQTT dan DDS tersebut dilakukan dengan memperhatikan dua parameter uji, yaitu delay dan drop packet. Kedua protokol tersebut akan diuji pada dua skenario jaringan. Yang pertama adalah pada jaringan low latency, seperti pada jaringan yang menggunakan media fiber optik. Sedangkan jaringan lainnya yang bersifat high latency. Hasil pengujian yang didapat, untuk parameter delay terbaik adalah DDS pada jaringan dengan low latency maupun high latency. Sedangkan untuk parameter drop packet, MQTT unggul dibandingkan DDS

Real-time modelling of DDS for event-driven applications

REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.The Data Distribution Service (DDS) standard defines a data-centric distribution middleware that supports the development of distributed real-time systems. To this end, the standard includes a wide set of configurable parameters to provide different degrees of Quality of Service (QoS). This paper presents an analysis of these QoS parameters when DDS is used to build reactive applications normally designed under an event-driven paradigm, and shows how to configure DDS to obtain predictable applications suitable to apply traditional schedulability analysis techniques.This work has been funded in part by the Spanish Government under grant number TIN2011-28567-C03-02 (HI-PARTES)

Real-Time QoS-Aware Vehicle Tracking: An Experimental and Comparative Study

AbstractRecently, web service became popular for Real-time Communication (RTC). It allows bi-directional, real-time communication between web clients and server. On the other hand, Data Distribution Service (DDS) middleware offers unified integration with high-performance due to its scalability, flexibility, real-time, mission-critical networks and rich QoS features. DDS is based on the publish/subscribe communication model. It improves RTC through its efficient and high-performance data delivery mechanism. This paper studies and investigates that how DDS is better for RTC. Experimental studies are conducted to compare text messaging using socket IO over DDS Web API. The result concerns the throughput satisfaction rate, round trip time and packet loss. In addition, we consider some of QoS of DDS during experimental work e.g. deadline, time based filter etc

Service Virtualisation of Internet-of-Things Devices: Techniques and Challenges

Service virtualization is an approach that uses virtualized environments to automatically test enterprise services in production-like conditions. Many techniques have been proposed to provide such a realistic environment for enterprise services. The Internet-of-Things (IoT) is an emerging field which connects a diverse set of devices over different transport layers, using a variety of protocols. Provisioning a virtual testbed of IoT devices can accelerate IoT application development by enabling automated testing without requiring a continuous connection to the physical devices. One solution is to expand existing enterprise service virtualization to IoT environments. There are various structural differences between the two environments that should be considered to implement appropriate service virtualization for IoT. This paper examines the structural differences between various IoT protocols and enterprise protocols and identifies key technical challenges that need to be addressed to implement service virtualization in IoT environments.Comment: 4 page