Uncertainty quantification through the Monte Carlo method in a cloud computing setting

  title={Uncertainty quantification through the Monte Carlo method in a cloud computing setting},
  author={Americo Cunha and Rafael Barbosa Nasser and Rubens Sampaio and H{\'e}lio C{\^o}rtes Vieira Lopes and Karin Koogan Breitman},

The uncertain cloud: State of the art and research challenges

Massive simulations using MapReduce model

Experiment based on variability analysis for simple electro- magnetic problem with over 10,000 scenarios proves that platform has nearly linear scalability with over 80% of theoretical maximum performance.

A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

The authors simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling, and show the superiority of the proposed algorithms in terms of customer satisfaction and surplus customer expectation.

zkQMC: Zero-Knowledge Proofs For (Some) Probabilistic Computations Using Quasi-Randomness

A technique for proving the integrity of certain randomized computations in non-interactive zero knowledge (NIZK) by replacing conventional randomness with low-discrepancy sequences and proves a new result using discrepancy theory to efficiently and soundly estimate the output of computations with uncertain data.

Sensitivity Analysis and Uncertainty Quantification of State-Based Discrete-Event Simulation Models Through a Stacked Ensemble of Metamodels

This work presents a novel approach for performing fast and thorough SA and UQ on a metamodel composed of a stacked ensemble of regressors that emulates the behavior of the base model.

Turboelectric Uncertainty Quantification and Error Estimation in Numerical Modelling

The results show that the electrical elements in turboelectric systems can have decent outcomes in statistical analysis, and the enhancement of the turboelectric system through electrical power optimisation management could lead to higher performance.

Visual Exploration Tools for Ensemble Clustering Analysis

Uncertainty Analysis is essential to support decisions, and it has been gaining attention in both visualization and machine learning communities —in the latter case, mainly because ensemble methods

Time-Aware Task Allocation for Cloud Computing Environment

A fairness algorithm called TATA is proposed to provide fairness among the leases in IaaS cloud and shows that TATA produces better response time for both the leases than the existing algorithm.

Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

This paper proposes a data-driven machine learning framework for parameter estimation and uncertainty quantification in epidemic models based on two key ingredients: (i) prior parameters learning via



High Performance Computing in the cloud: Deployment, performance and cost efficiency

A detailed comparison of HPC applications running on three cloud providers, Amazon EC2, Microsoft Azure and Rackspace, shows that HPC in the cloud can have a higher performance and cost efficiency than a traditional cluster, up to 27% and 41%, respectively.

Explorations in Monte Carlo Methods

Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems.

Monte Carlo Strategies in Scientific Computing

The strength of this book is in bringing together advanced Monte Carlo methods developed in many disciplines, including the Ising model, molecular structure simulation, bioinformatics, target tracking, hypothesis testing for astronomical observations, Bayesian inference of multilevel models, missing-data problems.

Numerically stable, single-pass, parallel statistics algorithms

This paper derives a series of formulas that allow for single-pass, yet numerically robust, pairwise parallel and incremental updates of both arbitrary-order centered statistical moments and co-moments and builds an open source parallel statistics framework that performs principal component analysis (PCA) in addition to computing descriptive, correlative, and multi-correlative statistics.

Design and implementation of a cloud computing service for finite element analysis

Note on a Method for Calculating Corrected Sums of Squares and Products

In many problems the "corrected sum of squares" of a set of values must be calculated i.e. the sum of squares of the deviations of the values about their mean. The most usual way is to calculate the

Cloud Computing Principles and Paradigms

This book is targeted for professional computer science developers and graduate students especially at Masters level and aims to identify potential research directions and technologies that will facilitate creation a global market-place of cloud computing services supporting scientific, industrial, business, and consumer applications.

Cloud Computing, A Practical Approach

This accessible book offers a broad introduction to cloud computing, reviews a wide variety of currently available solutions, and discusses the cost savings and organizational and operational benefits.

Monte Carlo Statistical Methods

In comparing nonparametric and parametric methods of time series analysis, the book summarizes the results of Carbon and Delecroix (1993), who considered several simulated autoregressive moving average (ARMA) processes.