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Displaying 1 - 25 of 65

Distributed Algorithm for Suppressing Epidemic Spread in Networks

June 4, 2018
Author(s)
Van Sy Mai, Abdella Battou, Kevin L. Mills
This paper considers problems related to suppressing epidemic spread over networks given limited curing resources. The spreading dynamic is captured by a susceptible-infected- susceptible model. The epidemic threshold and recovery speed are determined by

Evaluating Predictors of Congestion Collapse in Communication Networks

April 23, 2018
Author(s)
Christopher E. Dabrowski, Kevin L. Mills
Researchers model congestion in communication networks using a percolation process, where congestion spreads minimally before a critical load and expands rapidly afterwards. Some studies identify precursor signals arising near critical load, but none

A Layered and Aggregated Queuing Network Simulator for Detection of Abnormalities

December 3, 2017
Author(s)
Junfei Xie, Chenyan He, Yan Wan, Kevin L. Mills, Christopher E. Dabrowski
Driven by the needs to monitor, detect, and prevent catastrophic failures for complex information systems in real-time, we develop in this paper a discrete-time queuing network simulator. The dynamic model for the simulator abstracts network dynamics by

Using realistic factors to simulate catastrophic congestion events in a network

November 1, 2017
Author(s)
Kevin L. Mills, Christopher E. Dabrowski
With the rapid growth of the Internet, there has been increased interest in the use of computer models to study the dynamics of communication networks in the research literature. An important example of this has been the study of dramatic, but relatively

The Need for Realism when Simulating Network Congestion

April 2, 2016
Author(s)
Kevin L. Mills, Christopher E. Dabrowski
Many researchers use simulation to investigate network congestion, often finding congestion spread can be modeled as percolation, spreading slowly under increasing load until a critical point, then spreading quickly through the network. The researchers

The Influence of Realism on Congestion in Network Simulations

January 8, 2016
Author(s)
Kevin L. Mills, Christopher E. Dabrowski
Many researchers have used simulation to investigate the spread of congestion in networks. These researchers often find that congestion can be modeled as a percolation process, spreading slowly under increasing load until a critical point. After the

Assessing Effects of Asymmetries, Dynamics, and Failures on a Cloud Simulator

March 29, 2015
Author(s)
Kevin L. Mills, James J. Filliben, Christopher E. Dabrowski
We characterize the effects of asymmetries, dynamics, and failures when introduced into a cloud computing simulator, which had previously been characterized under static, homogeneous configurations with various patterns of demand and supply. We aim to

Effective and Scalable Uncertainty Evaluation for Large-Scale Complex System Applications

December 7, 2014
Author(s)
Kevin L. Mills, James J. Filliben, Junfei Xie, Yan Wan, Yi Zhou, Yu Lei
Effective uncertainty evaluation is a critical step toward real-time and robust decision-making for complex systems in uncertain environments. A Multivariate Probabilistic Collocation Method (M-PCM) was developed to effectively evaluate system uncertainty

Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models

October 28, 2013
Author(s)
Kevin L. Mills, Christopher E. Dabrowski, James J. Filliben, Sanford P. Ressler
Large infrastructures, such as clouds, can exhibit substantial outages, sometimes due to failure scenarios that were not considered during system design. We define a method that uses a genetic algorithm (GA) to search system simulations for parameter

Predicting Global Failure Regimes in Complex Information Systems

June 19, 2012
Author(s)
Christopher E. Dabrowski, James J. Filliben, Kevin L. Mills
Over the past five years, we investigated methods to characterize global behavior in large distributed systems and applied those methods to predict effects from deploying alternate distributed control algorithms. The methods we used assess global behaviors

Comparing VM-Placement Algorithms for On-Demand Clouds

November 29, 2011
Author(s)
Kevin L. Mills, James J. Filliben, Christopher E. Dabrowski
Much recent research has been devoted to investigating algorithms for allocating virtual machines (VMs) to physical machines (PMs) in infrastructure clouds. Many such algorithms address distinct problems, such as initial placement, consolidation, or

VM Leakage and Orphan Control in Open-Source Clouds

November 29, 2011
Author(s)
Christopher E. Dabrowski, Kevin L. Mills
Computer systems often exhibit degraded performance due to resource leakage caused by erroneous programming or malicious attacks, and computers can even crash in extreme cases of resource exhaustion. The advent of cloud computing provides increased

COMPARISON OF TWO DIMENSION-REDUCTION METHODS FOR NETWORK SIMULATION MODELS

October 5, 2011
Author(s)
Kevin L. Mills, James J. Filliben
Experimenters characterize the behavior of simulation models for data communications networks by measuring multiple responses under selected parameter combinations. The resulting multivariate data may include redundant responses reflecting aspects of a

Predicting Macroscopic Dynamics in Large Distributed Systems

July 18, 2011
Author(s)
Kevin L. Mills, James J. Filliben
In this paper, we outline an approach that can be used to predict macroscopic dynamics when new components are deployed in a large distributed system. Our approach combines two main methods: scale reduction and multidimensional data analysis techniques

An Efficient Sensitivity Analysis Method for Large Cloud Simulations

July 4, 2011
Author(s)
Kevin L. Mills
We describe Koala, an infrastructure Cloud simulator inspired by the Amazon Elastic Compute Cloud (EC2). We conduct a sensitivity analysis of Koala, revealing eight behavioral dimensions, which are influenced significantly by six parameters. Our findings

AN EFFICIENT SENSITIVITY ANALYSIS METHOD FOR NETWORK SIMULATION MODELS

December 7, 2010
Author(s)
Kevin L. Mills, James J. Filliben
Simulation models for data communications networks encompass numerous parameters that can each take on millions of values, presenting experimenters with a vast space of potential parameter combinations. To apply such simulation models experimenters face a

How to Model a TCP/IP Network Using Only 20 Parameters

December 5, 2010
Author(s)
Kevin L. Mills, Edward J. Schwartz, Jian Yuan
Most simulation models for data communication networks encompass hundreds of parameters that can each take on millions of values. Such models are difficult to understand, parameterize and investigate. This paper explains how to model a modern data

Study of Proposed Internet Congestion-Control Mechanisms

May 17, 2010
Author(s)
Kevin L. Mills, James J. Filliben, Dong Y. Cho, Edward J. Schwartz, Daniel I. Genin
This study describes a coherent set of modeling and analysis methods to investigate the behavior of large distributed systems. The methods are applied to compare several proposed Internet congestion-control mechanisms operating under a wide range of

On Maximizing Provider Revenue in Market-based Compute Grids

April 17, 2008
Author(s)
Vladimir V. Marbukh, Kevin L. Mills
Market-based compute grids encompass service providers offering limited resources to potential users with varying demands and willingness to pay. Providers face difficult decisions about which jobs to admit and when to schedule admitted jobs. For this