EQS: an elastic and scalable message queue for the cloud

With the emergence of cloud computing, on-demand resources usage is made possible. This allows applications to elastically scale out according to the load. One design pattern that suits this paradigm is the event-driven architecture (EDA) in which messages are sent asynchronously between distributed application instances using message queues. However, existing message queues are only able to scale for a certain number of clients and are not able to scale out elastically. We present the Elastic Queue Service (EQS), an elastic message queue architecture and a scaling algorithm which can be adapted to any message queue in order to make it scale elastically. EQS architecture is layered onto multiple distributed components and its management components can be integrated with the cloud infrastructure management. We have implemented a prototype of EQS and deployed it on a cloud infrastructure. A series of load testings have validated our elastic scaling algorithm and show that EQS is able to scale out in order to adapt to an applied load. We then discuss about the elastic scaling of the management layers of EQS and their possible integration with the cloud infrastructure management.

Nam-Luc Tran, Sabri Skhiri, and Esteban Zimány, EQS: An Elastic and Scalable Message Queue for the Cloud, proceedings of the 3rd International IEEE conference on Cloud computing technology and science (IEEE CloudCom 2011), Athens, Greece, November 2011.

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Governance issues on heavy models in an industrial context

SWIFT is a member-owned cooperative providing secure messaging capabilities to the financial services industry. One critical mission of SWIFT is the standardization of the message flows between the industry players. The model-driven approach naturally came as a solution to the management of these message definitions. However, one of the most important challenges that SWIFT has been facing is the global governance of the message repository and the management of each element. Nowadays modeling tools exist but none of them enables the management of the complete life-cycle of the message models. In this paper wepresent the challenges that SWIFT had to face in the development of a dedicated platform.


Sabri Skhiri, Marc Delbaere, Yves Bontemps, Grégoire de Hemptinne, and Nam-Luc Tran, Governance issues on heavy models in an industrial context. Advances in Conceptual Modeling. Recent Developments and New Directions ER 2011, Brussels, Belgium, November 2011.

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Measuring elasticity for cloud databases

The rise of the Internet and the multiplication of data sources have multiplied the number of “Bigdata” storage problems. These data sets are not only very big but also tend to grow very fast, sometimes in a short period. Distributed databases that work well for such data sets need to be not only scalable but also elastic to ensure a fast response to growth in demand of computing power or storage. The goal of this article is to present measurement results that characterize the elasticity of three databases. We have chosen Cassandra, HBase, and mongoDB as three representative popular horizontally scalable NoSQL databases that are in production use. We have made measurements under realistic loads up to 48 nodes, using the Wikipedia database to create our dataset and using the Rackspace cloud infrastructure. We define precisely our methodology and we introduce a new dimensionless measure for elasticity to allow uniform comparisons of different databases at different scales. Our results show clearly that the technical choices taken by the databases have a strong impact on the way they react when new nodes are added to the clusters.

Thibault Dory, Boris Mejías, Peter Van Roy, and Nam-Luc Tran, Measuring Elasticity for Cloud Databases, proceedings of the Cloud Computing 2011 (Second International Conference on Cloud Computing, GRIDs, and Virtualization), Rome, Italy, September 2011.

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