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The significantly high energy consumption of data centers constitutes a major load on the smart power grid. Data center demand response is a promising solution to incentivize the cloud providers to adapt their consumption to the power grid conditions. These policies not only mitigate the operational stability issues of the smart grid but also potentially decrease the electricity bills of cloud providers. Cloud providers can improve their contribution and reduce their energy cost by collaboratively managing their workload. Through cooperation in the form of cloud federations, providers can spatially migrate their workload to better utilize the benefits provided by demand response schemes over multiple locations. To this end, this work considers an interaction system between the independent cloud providers and the corresponding smart grid utilities in the context of a demand response program. Leveraging the cooperative game theory, this paper presents a federation formation among the cloud providers in the presence of a location-dependent demand response program. A distributed algorithm that is coupled with an optimal workload allocation problem is applied. The effect of the federation's formation on the clouds' profits and on the smart grid performance is analyzed through simulation. Simulation results show that cooperation increases the clouds' profits as well as the smart grid performance compared to the noncooperative case. INDEX TERMS Cloud federation, coalitional game, demand response, smart grid.

Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs in the last few years, several issues remain to be solved. This paper reviews the literature on the game-theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative models and improve the GAN's performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, Games of GANs and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature. We then explore the objectives of each category and discuss recent works in each class. Finally, we discuss the remaining challenges in this field and present future research directions.

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