3,001 research outputs found
Autoregressive Time Series Forecasting of Computational Demand
We study the predictive power of autoregressive moving average models when
forecasting demand in two shared computational networks, PlanetLab and Tycoon.
Demand in these networks is very volatile, and predictive techniques to plan
usage in advance can improve the performance obtained drastically.
Our key finding is that a random walk predictor performs best for
one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential
smoothing models perform better for two and three-step-ahead forecasts. A Monte
Carlo bootstrap test is proposed to evaluate the continuous prediction
performance of different models with arbitrary confidence and statistical
significance levels. Although the prediction results differ between the Tycoon
and PlanetLab networks, we observe very similar overall statistical properties,
such as volatility dynamics
Negative externalities and evolutionary implementation
game theory;externalities
Pigouvian pricing and stochastic evolutionary implementation
pricing;game theory
Survival of dominated strategies under evolutionary dynamics
We show that any evolutionary dynamic that satisfies three mild requirements—
continuity, positive correlation, and innovation—does not eliminate strictly dominated
strategies in all games. Likewise, we demonstrate that existing elimination results
for evolutionary dynamics are not robust to small changes in the specifications of the
dynamics
Complexity of Determining Nonemptiness of the Core
Coalition formation is a key problem in automated negotiation among
self-interested agents, and other multiagent applications. A coalition of
agents can sometimes accomplish things that the individual agents cannot, or
can do things more efficiently. However, motivating the agents to abide to a
solution requires careful analysis: only some of the solutions are stable in
the sense that no group of agents is motivated to break off and form a new
coalition. This constraint has been studied extensively in cooperative game
theory. However, the computational questions around this constraint have
received less attention. When it comes to coalition formation among software
agents (that represent real-world parties), these questions become increasingly
explicit.
In this paper we define a concise general representation for games in
characteristic form that relies on superadditivity, and show that it allows for
efficient checking of whether a given outcome is in the core. We then show that
determining whether the core is nonempty is -complete both with
and without transferable utility. We demonstrate that what makes the problem
hard in both cases is determining the collaborative possibilities (the set of
outcomes possible for the grand coalition), by showing that if these are given,
the problem becomes tractable in both cases. However, we then demonstrate that
for a hybrid version of the problem, where utility transfer is possible only
within the grand coalition, the problem remains -complete even
when the collaborative possibilities are given
Notes on Cloud computing principles
This letter provides a review of fundamental distributed systems and economic
Cloud computing principles. These principles are frequently deployed in their
respective fields, but their inter-dependencies are often neglected. Given that
Cloud Computing first and foremost is a new business model, a new model to sell
computational resources, the understanding of these concepts is facilitated by
treating them in unison. Here, we review some of the most important concepts
and how they relate to each other
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