International audienceLarge scale distributed systems such as Grids are difficult to study from theoretical models and simulators only. Most Grids deployed at large scale are production platforms that are inappropriate research tools because of their limited reconfiguration, control and monitoring capabilities. In this paper, we present Grid'5000, a 5000 CPU nation-wide infrastructure for research in Grid computing. Grid'5000 is designed to provide a scientific tool for computer scientists similar to the large-scale instruments used by physicists, astronomers, and biologists. We describe the motivations, design considerations, architecture, control, and monitoring infrastructure of this experimental platform. We present configuration examples and performance results for the reconfiguration subsystem

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To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

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Among existing grid middleware approaches, one simple, powerful, and flexible approach consists of using servers available in different administrative domains through the classical client-server or Remote Procedure Call (RPC) paradigm. Network Enabled Servers implement this model also called GridRPC. Clients submit computation requests to a scheduler whose goal is to find a server available on the grid. The aim of this paper is to give an overview of a middleware developed in the GRAAL team called DIET 1 . DIET (Distributed Interactive Engineering Toolbox) is a hierarchical set of components used for the development of applications based on computational servers on the grid. RésuméParmi les intergiciels de grilles existants, une approche simple, flexible et performante consisteà utiliser des serveurs disponibles dans des domaines administratifs différentsà travers le paradigme classique de l'appel de procédureà distance (RPC). Les environnements de ce type, connus sous le terme de Network Enabled Servers, implémentent ce modèle appelé GridRPC. Des clients soumettent des requêtes de calculà un ordonnanceur dont le but consisteà trouver un serveur disponible sur la grille. Le but de cet article est de donner un tour d'horizon d'un intergiciel développé dans le projet GRAAL appelé DIET 2 . DIET (Distributed Interactive Engineering Toolbox) est un ensemble hiérarchique de composants utilisés pour le développement d'applications basées sur des serveurs de calcul sur la grille.

As fog computing brings compute and storage resources to the edge of the network, there is an increasing need for automated placement (i.e., selection of hosting devices) to deploy distributed applications. Such a placement must conform to applications' resource requirements in a heterogeneous fog infrastructure. The placement decision-making is further complicated by Internet of Things (IoT) applications that are tied to geographical locations of physical objects/things. This paper presents a model, an objective function, and a mechanism to address the problem of placing distributed IoT applications in the fog. Based on a backtrack search algorithm and accompanied heuristics, the proposed mechanism is able to deal with large scale problems, and to efficiently make placement decisions that fit the objective-to lower placed applications' response time. The proposed approach is validated through comparative simulations of different combinations of the algorithms and heuristics on varying sizes of infrastructures and applications. CCS CONCEPTS • Software and its engineering → Distributed systems organizing principles;

International audienceThe Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world traces from Cloud and Grid platforms. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources

International audienceThis article is devoted to the run-time redistribution of one-dimensional arrays that are distributed in a block-cyclic fashion over a processor grid. While previous studies have concentrated on efficiently generating the communication messages to be exchanged by the processors involved in the redistribution, we focus on the scheduling of those messages: how to organize the message exchanges into "structured" communication steps that minimize contention. We build upon results of Walker and Otto, who solved a particular instance of the problem, and we derive an optimal scheduling for the most general case, namely, moving from a CYCLIC(r) distribution on a P-processor grid to a CYCLIC(s) distribution on a Q-processor grid, for arbitrary values of the redistribution parameters P, Q, r, and