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Content Filter Propagation across TCP WAN connection

Hi,

We have a DataWriter in one network sending data to a DataReader in another network over TCP.  We have configured the Participant QoS on both DataWriter's and DataReader's side to use nddstransporttcp transport library. 

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Calculating Queuing Delay for a DataReader

Hi,

I am trying to understand how I can calculate the queuing delay of messages for a DataReader's receive queue. For example, if a message entered a DataReader's receive queue at time-stamp t1 and the take() operation was called at time-stamp t2, I am interested in calculating (t2-t1).  Similarly, I would like to calculate the queuing delay for DataWriter's send queue. 

Can you please let me know if there are methods/fields exposed by the DDS API which can help me in this regard? 

Thank you,

Shweta 

Receive Queue Reference:

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Reactive Stream Processing for Data-centric Publish/Subscribe

The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fast-moving, heterogeneous sensor-generated data. Such data streams are often unbounded and must be processed in a distributed and parallel manner to ensure timely processing and delivery to interested subscribers.

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2015
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PDF icon rxdds_debs_2015_submission.pdf

out of order delivery of samples

Hi,

We are getting data samples out-of-order on the subscriber side, when we use a QoS profile that is an extension of BuiltInQosLib-  Generic.StrictReliable.HighThroughput or when using a profile that is an extension of Generic.AutoTuning. (I have attached the xml QoS file we are using) 

This occurs even when batching is disabled. 

On the other hand, if we use the default QoS settings then we get samples in order. 

Can you please suggest why this behavior is occuring? 

 

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RTI license for PerfTest Tool.

hi, 

I had worked with RTI PerfTest tool in the past and recently got the opportunity to extend the middleware agnositc test-harness that is provided by PerfTest tool, for testing other open-source messaging libraries like Zero-mq and Kafka. 

Since these messaging libraries are not fully functional pub-sub middleware technologies, I had modified the harness to include synchronization between publishers & subscribers and to circumvent some thread-safety issues. 

The license statement included in PerfTest code is as follows: 

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A Cloud-enabled Coordination Service for Internet-scale OMG DDS applications

http://www.dre.vanderbilt.edu/~kyoungho/papers/DEBS2014_Poster.pdf

The OMG Data Distribution Service (DDS), which is a standard specification for data-centric publish/subscribe communications, has shown promise for use in internet of things (IoT) applications because of its loosely coupled and scalable nature, and support for multiple QoS properties, such as reliable and real-time message delivery in dynamic environments.

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Publication Year: 
2014
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Content-based Filtering Discovery Protocol (CFDP): Scalable and Efficient OMG DDS Discovery Protocol

The OMG Data Distribution Service (DDS) has been deployed in many mission-critical systems and increasingly in Internet of Things (IoT) applications since it supports a loosely-coupled, data-centric publish/subscribe paradigm with a rich set of quality-of-service (QoS) policies. Effective data communication between publishers and subscribers requires dynamic and reliable discovery of publisher/subscriber endpoints in the system, which DDS currently supports via a standardized approach called the Simple Discovery Protocol (SDP).

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Publication Year: 
2014
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Scalable Reactive Stream Processing Using DDS and Rx

Event-driven design is fundamental to developing resilient, responsive, and scalable reactive systems as it supports asynchrony and loose coupling. The OMG Data Distribution Service (DDS) is a proven event-driven technology for building data-centric reactive systems because it provides the primitives for decoupling system components with respect to time, space, quality-of-service, and behavior. DDS, by design, supports distribution scalability. However, with increasing core count in CPUs, building multicore-scalable reactive systems remains a challenge.

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Publication Year: 
2014
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PDF icon rx4dds-techreport.pdf

Supporting End-to-end Scalability and Real-time Event Dissemination in the OMG Data Distribution Service over Wide Area Networks

http://www.dre.vanderbilt.edu/~schmidt/PDF/elsarticle-journal.pdf

Assuring end-to-end quality-of-service (QoS) in distributed real-time and embedded (DRE) systems is hard due to the heterogeneity and scale of communication networks, transient behavior, and the lack of mechanisms that holistically schedule different resources end-to-end.

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2013
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PDF icon omg_dds_wan_hakiri_schmidt.pdf

Elastic Infrastructure to Support Computing Clouds for Large-scale Cyber-Physical Systems

http://www.dre.vanderbilt.edu/~schmidt/PDF/ISORC_2014.pdf

Large-scale cyber-physical systems (CPS) in mission-critical areas such as transportation, health care, energy, agriculture, defense, homeland security, and manufacturing, are becoming increasingly interconnected and interdependent. These types of CPS are unique in their need to combine rigorous control over timing and physical properties, as well as functional ones, while operating dynamically, reliably and affordably over significant scales of distribution, resource consumption, and utilization.

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Publication Year: 
2014
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