126 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
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
WHO-IS: Wireless Hetnet Optimization using Impact Selection
We propose a method to first identify users who have the most negative impact
on the overall network performance, and then offload them to an orthogonal
channel. The feasibility of such an approach is verified using real-world
traces, network simulations, and a lab experiment that employs multi-homed
wireless stations. In our experiment, as offload target, we employ LiFi IR
transceivers, and as the primary network we consider a typical Enterprise Wi-Fi
setup. We found that a limited number of users can impact the overall
experience of the Wi-Fi network negatively, hence motivating targeted
offloading. In our simulations and experiments we saw that the proposed
solution can improve the collision probability with 82% and achieve a 61
percentage point air utilization improvement compared to random offloading,
respectively
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