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DICE H2020
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Developing Data-Intensive Applications with Iterative Quality Enhancements
Developing Data-Intensive Applications with Iterative Quality Enhancements

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Welcome to DICE!

DICE is a collaborative research project, between 9 partner organisations.

The project has the aim of developing data-intense applications with iterative quality enhancements. DICE will offer a novel UML profile and tools that will help software designers reasoning about reliability, safety and efficiency of Big Data applications.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644869.

For more information, visit the project website - www.dice-h2020.eu/

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We're very pleased to share with you this presentation of the paper published at the QUDOS2015 conference titled "SPACE4Clouds a DevOps Environment for Multi-Clouds Applications"

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We're very pleased to share with you this presentation of the paper published at the QUDOS2015 conference titled "SPACE4Clouds a DevOps Environment for Multi-Clouds Applications"

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We're very pleased to share with you this presentation of the paper published at the QUDOS2015 conference titled "SPACE4Clouds a DevOps Environment for Multi-Clouds Applications"

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We are pleased to let you all know that the DICE-sponsored paper "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data" has received the best paper award at ACM/SPEC ICPE 2016 (https://icpe2016.spec.org/)

Authored by Weikun Wang, Giuliano Casale, Ajay Kattepur and Manoj Nambiar, the paper investigates maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times.

Resource demand estimation is essential for the application of analytical models, such as queueing networks, to real-world systems.

Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems.

One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model.

Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application.

dice-project.eu
#bigdata

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Find out all about the #DICE project in our latest video available now online https://www.youtube.com/watch?v=GelbjpCka8E&feature=youtu.be&a #bigdata

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Our first video is now available on Youtube https://youtu.be/GelbjpCka8E 

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