International audienceThe Internet of Things (IoT) has become a reality with the availability of chatty embedded devices. The huge amount of data generated by things must be analysed with models and technologies of the " Big Data An-alytics " , deployed on Cloud platforms. The CIRUS project aims to deliver a generic and elastic cloud-based framework for Ubilytics (ubiquitous big data analytics). The CIRUS framework collects and analyses IoT data for Machine to Machine services using Component-off-the-Shelves (COTS) such as IoT gateways, Message brokers or Message-as-a-Service providers and Big Data analytics platforms deployed and reconfigured dynamically with Roboconf. In this paper, we demonstrate and evaluate the genericity and elasticity of CIRUS with the deployment of an Ubilytics use case using a real dataset based on records originating from a practical source

Abstract. Load testing has always been a crucial and expensive activity for software companies. Classical solutions are a real burden to setup statically and their cost are prohibitive in terms of human and hardware resources. Cloud computing brings new opportunities to stress application scalability as load testing solutions can be provided on demand by the cloud. This paper describes a Benchmark-as-a-Service solution that scales automatically the load injection platform and eases its setup according to load profiles. Our approach is based on: (i) the virtualization of the Benchmarking platform to enable the injector's self-scalability, (ii) an online calibration mechanism to characterize injector capacity and impact on the benched application, (iii) a provisioning solution to scale the load injection platform sufficiently ahead of time. We also report experiments on a benchmark that shows the benefits in terms of cost and resources savings.