This paper investigates reactive elasticity in stream processing environments where the performance goal is to analyze large amounts of data with low latency and minimum resources. Working in the context of Apache Storm, we propose an elastic management strategy which modulates the parallelism degree of applications' components while explicitly addressing the hierarchy of execution containers (virtual machines, processes and threads). We show that provisioning the wrong kind of container may lead to performance degradation and propose a solution that provisions the least expensive container (with minimum resources) to increase performance. We describe our monitoring metrics and show how we take into account the specifics of an execution environment. We provide an experimental evaluation with real-world applications which validates the applicability of our approach.

Today's systems are often distributed, and connecting their different components can be challenging. Message-Oriented-Middleware (MOM) is a popular tool to insure simple and reliable communication. With the ever growing loads of today's applications, MOMs needs to be scalable. But as the load changes, static scalability often underuses the resources it requires. This paper presents an elastic message queuing system leveraging cloud's on-demand resource provisioning, which allows the use of just enough resources to handle the current load. We will detail when and how provisioning decisions are made, and show the result of our system's evaluation on Amazon EC2 public cloud. This work is based on Joram, an open-source JMS compliant MOM and is now part of its distribution on OW2 consortium's website.

In the coming years, cloud environments will increasingly face energy saving issues. While consolidating the virtual machines running in a cloud is a well-accepted solution to reduce the energy consumption, ensuring the scalability of the consolidation service remains a challenging issue. In this paper, we propose an elastic consolidation service that scales according to the dynamic needs of the cloud environment. Our proposition is based on (i) virtualizing the consolidation manager, (ii) partitioning the consolidation work and (iii) regulating the consolidation scalability through an autonomic control loop. Our proposition has been tested and validated through several experiments.