7,150 research outputs found
Proactive cloud management for highly heterogeneous multi-cloud infrastructures
Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework
Charging Games in Networks of Electrical Vehicles
In this paper, a static non-cooperative game formulation of the problem of
distributed charging in electrical vehicle (EV) networks is proposed. This
formulation allows one to model the interaction between several EV which are
connected to a common residential distribution transformer. Each EV aims at
choosing the time at which it starts charging its battery in order to minimize
an individual cost which is mainly related to the total power delivered by the
transformer, the location of the time interval over which the charging
operation is performed, and the charging duration needed for the considered EV
to have its battery fully recharged. As individual cost functions are assumed
to be memoryless, it is possible to show that the game of interest is always an
ordinal potential game. More precisely, both an atomic and nonatomic versions
of the charging game are considered. In both cases, equilibrium analysis is
conducted. In particular, important issues such as equilibrium uniqueness and
efficiency are tackled. Interestingly, both analytical and numerical results
show that the efficiency loss due to decentralization (e.g., when cost
functions such as distribution network Joule losses or life of residential
distribution transformers when no thermal inertia is assumed) induced by
charging is small and the corresponding "efficiency", a notion close to the
Price of Anarchy, tends to one when the number of EV increases.Comment: 8 pages, 4 figures, keywords: Charging games - electrical vehicle -
distribution networks - potential games - Nash equilibrium - price of anarch
Fighting Bandits with a New Kind of Smoothness
We define a novel family of algorithms for the adversarial multi-armed bandit
problem, and provide a simple analysis technique based on convex smoothing. We
prove two main results. First, we show that regularization via the
\emph{Tsallis entropy}, which includes EXP3 as a special case, achieves the
minimax regret. Second, we show that a wide class of
perturbation methods achieve a near-optimal regret as low as if the perturbation distribution has a bounded hazard rate. For example,
the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this
key property.Comment: In Proceedings of NIPS, 201
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