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  1. null (Ed.)
  2. The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras. 
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  3. Enterprise and Cloud environments are rapidly evolving with the use of lightweight virtualization mechanisms such as containers. Containerization allow users to deploy applications in any environment faster and more efficiently than using virtual machines. However, most of the work in this area focused on Linux-based containerization such as Docker and LXC and other mature solutions such as FreeBSD Jails have not been adopted by production-ready environments. In this work we explore the use of FreeBSD virtualization and provide a comparative study with respect to Linux containerization using Apache Spark. Preliminary results show that, while Linux containers provide better performance, FreeBSD solutions provide more stable and consistent results. 
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  4. Scientific simulation workflows executing on very large scale computing systems are essential modalities for scientific investigation. The increasing scales and resolution of these simulations provide new opportunities for accurately modeling complex natural and engineered phenomena. However, the increasing complexity necessitates managing, transporting, and processing unprecedented amounts of data, and as a result, researchers are increasingly exploring data-staging and in-situ workflows to reduce data movement and data-related overheads. However, as these workflows become more dynamic in their structures and behaviors, data staging and in-situ solutions must evolve to support new requirements. In this paper, we explore how the service-oriented concept can be applied to extreme-scale in-situ workflows. Specifically, we explore persistent data staging as a service and present the design and implementation of DataSpaces as a Service, a service-oriented data staging framework. We use a dynamically coupled fusion simulation workflow to illustrate the capabilities of this framework and evaluate its performance and scalability. 
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