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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.

Joint work with Zhentao Li and Adrian Vetta.

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

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.