Abstract-Conducting experiments in large-scale distributed systems is usually time-consuming and labor-intensive. Uncontrolled external load variation prevents to reproduce experiments and such systems are often not available to the purpose of research experiments, e.g., production or yet to deploy systems. Hence, many researchers in the area of distributed computing rely on simulation to perform their studies. However, the simulation of large-scale computing systems raises several scalability issues, in terms of speed and memory. Indeed, such systems now comprise millions of hosts interconnected through a complex network and run billions of processes. Most simulators thus trade accuracy for speed and rely on very simple and easy to implement models. However, the assumptions underlying these models are often questionable, especially when it comes to network modeling.In this paper, we show that, despite a widespread belief in the community, achieving high scalability does not necessarily require to resort to overly simple models and ignore important phenomena. We show that relying on a modular and hierarchical platform representation, while taking advantage of regularity when possible, allows us to model systems such as data and computing centers, peer-to-peer networks, grids, or clouds in a scalable way. This approach has been integrated into the opensource SIMGRID simulation toolkit. We show that our solution allows us to model such systems much more accurately than other state-of-the-art simulators without trading for simulation speed. SIMGRID is even sometimes orders of magnitude faster.

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Enforcing security properties in a Cloud is a difficult task, which requires expertise. However, it is not the only securityrelated challenge met by a company migrating to a Cloud environment. Indeed, the tenant must also have assurance that the requested security properties have effectively been enforced. Therefore, the Cloud provider has to offer a way of monitoring the security. In this paper, we present a solution to express the assurance properties based on the security requirements of the tenant and to deploy these assurance properties. First, we introduce a language that expresses the assurance based on the tenant's security requirements. Secondly, we propose an infrastructure that deploys the assurance in a Cloud environment. This solution aims to be easy to use: the assurance directly results from the high-level expression of the tenant's security requirements, and no additional action is needed from the tenant. Consequently, we address one of the greatest drawback of security and assurance -the complexity of their configuration -while providing a complete assurance mechanism.

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Experiments play an important role in parallel and distributed computing. Simulation is a common experimental technique that relies on abstractions of the tested application and execution environment but offers reproducibility of results and fast exploration of numerous scenarios. This article focuses on setting up the experimental environment of a simulation run. First we analyze the requirements expressed by different research communities. As the existing tools of the literature are too specific, we then propose a more generic experimental environment synthesizer called SIMULACRUM. This tool allows its users to select a model of a currently deployed computing grid or generate a random environment. Then the user can extract a subset of it that fulfills his/her requirements. Finally the user can export the corresponding XML representation.

Identifying and inferring performances of a network topology is a well known problem. Achieving this by using only end-to-end measurements at the application level is known as network tomography. When the topology produced reflects capacities of sets of links with respect to a metric, the topology is called a Metric-Induced Network Topology (MINT). Tomography producing MINT has been widely used in order to predict performances of communications between clients and server.Nowadays grids connect up to thousands communicating resources that may interact in a partially or totally coordinated way. Consequently, applications running upon this kind of platform often involve massively concurrent bulk data transfers. This implies that the client/server model is no longer valid. In this paper, we present MINTCar, a tool which is able to discover metric induced network topology using only end-to-end measurements for paths that do not necessarily share neither a common source nor a common destination.

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