Coalition games on interaction graphs by Nicolas Bousquet (Gscop)
– June 15, 2017
We consider cooperative games where the viability of a coalition is determined by whether or not its members have the ability to communicate amongst themselves. This necessary condition for viability was proposed by Myerson and is modeled via an interaction graph; a coalition S of vertices is then viable if and only if the induced graph S is connected.
The non-emptiness of the core of a coalition game can be tested by a well-known covering LP. Moreover, the integrality gap of its dual packing LP defines exactly the multiplicative least-core and the relative cost of stability of the coalition game. This gap is upper bounded by the packing-covering ratio which is known to be at most the treewidth of the interaction graph plus one.
We examine the packing-covering ratio and integrality gaps of graphical coalition games in more detail. First we introduce a new graph parameter, called the vinewidth (a parameter derived from the treewidth), which characterizes the worst packing-covering ratio. Then we will show that this new parameter correctly evaluates both primal and dual integrality gaps.
Malcom Egan Seminar : "Mechanism design in on-demand transport"
– June 22, 2017
Abstract: Uber is one of several recent companies adopting a business model that lies in stark contrast with the standard approach used by taxi services. Underlying Uber's business model is a new architecture--based on a market mechanism--which governs how commuters, drivers, and the company interact with each other. In this talk, we develop a new general model for on-demand transport networks with self-interested passengers and drivers. With this model, we introduce market mechanisms to allocate and price journeys, as well as the market formation subproblem. By analysis and simulation, we characterize the performance of the mechanisms and discuss insights using data obtained from a real on-demand transport provider.
Malcolm Egan received the B.E. degree in electrical engineering from the University of Queensland, Brisbane, Australia, in 2009 and the Ph.D. in electrical engineering from the University of Sydney, Sydney, Australia, in 2014. In the years 2014-2016, he was a Postdoctoral Researcher in the Department of Computer Science, Czech Technical University in Prague, Czech Republic and in the Laboratoire de Mathématiques, Université Blaise Pascal, Clermont-Ferrand, France. He is now a Postdoctoral Researcher in CITI Lab, INSA-Lyon, INRIA, Université de Lyon. His research interests include optimization theory, mechanism design, information theory and statistical signal processing, as well as their applications.
A stochastic approach for optimizing green energy consumption in distributed clouds by Fanny Dufossé (Inria)
– June 29, 2017
A stochastic approach for optimizing green energy consumption in distributed clouds
The energy drawn by Cloud data centers is reaching worrying levels, thus inciting providers to install on-site green energy producers, such as photovoltaic panels. Considering distributed Clouds, workload managers need to geographically allocate virtual machines according to the green production in order not to waste energy. In this paper, we propose SAGITTA: a Stochastic Approach for Green consumption In disTributed daTA centers. We show that compared to the optimal solution, SAGITTA consumes 4% more brown energy, and wastes only 3.14% of the available green energy, while a traditional round-robin solution consumes 14.4% more energy overall than optimum, and wastes 28.83% of the available green energy.
Séminaire Josu Doncel : Under-Approximation Computation Through Optimal Control
– July 6, 2017
Title: Under-Approximation Computation Through Optimal Control
Abstract: Under-approximation provides a subset of the reachable set of an uncertain dynamical system which can then be used to formally falsify properties of quantitative models. Using Pontryagin’s principle, our approach computes an under-approximation for a linear combination of state variables of nonlinear ordinary differential equations and time-varying uncertainties. By a numerical comparison against state-of-the-art tools Flow^∗ and CORA, we show that our methodology provides tight under-approximations in benchmarks, and that it can scale to models that are out of reach with these over-approximation techniques.
I/O performance for HPC: finding the right access pattern and avoiding interference by Francieli Zanon-Boito
– July 6, 2017
Title:
I/O performance for HPC: finding the right access pattern and avoiding interference
Abstract:
Scientific applications are executed in a high performance computing (HPC) environment, where a parallel file system (PFS) provides access to a shared storage infrastructure. The key characteristic of these systems is the use of multiple storage servers, from where data can be obtained by the clients in parallel. The performance observed by applications when accessing a PFS is directly affected by the way they perform this access, i.e. their access pattern.
In this seminar, I'll discuss my work with the Ondes3D seismic simulation, which was focused into changing the application's access pattern to improve I/O performance without changing the output format. Moreover, I'll discuss my previous and current work on I/O scheduling at different levels of the I/O stack, pointing current challenges for future work.
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