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Displaying 1-24 out of 24 total
Predicting the Performance of Virtual Machine Migration
Found in: Modeling, Analysis, and Simulation of Computer Systems, International Symposium on
By Sherif Akoush, Ripduman Sohan, Andrew Rice, Andrew W. Moore, Andy Hopper
Issue Date:August 2010
pp. 37-46
With the ability to move virtual machines between physical hosts, live migration is a core feature of virtualisation. However for migration to be useful, deployable feature on a large (datacentre) scale, we need to predict migration times with accuracy. In...
Characterizing 10 Gbps network interface energy consumption
Found in: 2010 IEEE 35th Conference on Local Computer Networks (LCN 2010)
By R Sohan,A Rice,Andrew W Moore,K Mansley
Issue Date:October 2010
pp. 268-271
This paper quantifies the energy consumption in six 10 Gbps and four 1 Gbps interconnects at a fine-grained level, introducing two metrics for calculating the energy efficiency of a network interface from the perspective of network throughput and host CPU ...
NetFPGA SUME: Toward 100 Gbps as Research Commodity
Found in: IEEE Micro
By Noa Zilberman,Yury Audzevich,G. Adam Covington,Andrew W. Moore
Issue Date:September 2014
pp. 32-41
The demand-led growth of datacenter networks has meant that many constituent technologies are beyond the research community's budget. NetFPGA SUME is an FPGA-based PCI Express board with I/O capabilities for 100 Gbps operation as a network interface card, ...
Making Logistic Regression a Core Data Mining Tool with TR-IRLS
Found in: Data Mining, IEEE International Conference on
By Paul Komarek, Andrew W. Moore
Issue Date:November 2005
pp. 685-688
Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiers are accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these requirements....
Learning to Recognize Time Series: Combining ARMA models with memory-based learning
Found in: Computational Intelligence in Robotics and Automation, IEEE International Symposium on
By Kan Deng, Andrew W. Moore, Michael C. Nechyba
Issue Date:June 1997
pp. 246
For a given time series observation sequence, we can esti- mate the parameters of the AutoRegression Moving Average (ARMA) model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vec...
Architecture for an open source network tester
Found in: 2013 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)
By Muhammad Shahbaz,Gianni Antichi,Yilong Geng,Noa Zilberman,Adam Covington,Marc Bruyere,Nick Feamster,Nick McKeown,Bob Felderman,Michaela Blott,Andrew W. Moore,Philippe Owezarski
Issue Date:October 2013
pp. 123-124
To make networks more reliable, enormous resources are poured into all phases of the network-equipment lifecycle. The process starts early in the design phase when simulation is used to verify the correctness of a design, and continues through manufacturin...
R2D2: bufferless, switchless data center networks using commodity ethernet hardware
Found in: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM (SIGCOMM '13)
By Andrew W. Moore, Malte Schwarzkopf, Matthew P. Grosvenor
Issue Date:August 2013
pp. 507-508
Modern data centers commonly run distributed applications that require low-latency communication, and whose performance is critical to service revenue. If as little as one machine in 10,000 is a latency outlier, around 18% of requests will experience high ...
Signposts: end-to-end networking in a world of middleboxes
Found in: Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication (SIGCOMM '12)
By Amir Chaudhry, Andrew W. Moore, Andrius Aucinas, Anil Madhavapeddy, Charalampos Rotsos, Jon Crowcroft, Narseo Vallina-Rodriguez, Richard Mortier, Sebastian Probst Eide, Steven Hand
Issue Date:August 2012
pp. 83-84
This demo presents Signposts, a system to provide users with a secure, simple mechanism to establish and maintain communication channels between their personal cloud of named devices. Signpost names exist in the DNSSEC hierarchy, and resolve to secure end-...
Supporting novel home network management interfaces with openflow and NOX
Found in: Proceedings of the ACM SIGCOMM 2011 conference on SIGCOMM (SIGCOMM '11)
By Alexandros Koliousis, Andrew W. Moore, Ben Bedwell, Charalampos Rotsos, Joseph Sventek, Kevin Glover, Richard Mortier, Tom Lodge, Tom Rodden
Issue Date:August 2011
pp. 464-465
The Homework project has examined redesign of existing home network infrastructures to better support the needs and requirements of actual home users. Integrating results from several ethnographic studies, we have designed and built a home networking platf...
Experience with high-speed automated application-identification for network-management
Found in: Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS '09)
By Andrew W. Moore, Marco Canini, Martin Zadnik, Wei Li
Issue Date:October 2010
pp. 209-218
AtoZ, an automatic traffic organizer, provides control of how network-resources are used by applications. It does this by combining the high-speed packet processing of the NetFPGA with an efficient method for application-behavior labeling. AtoZ can control...
Motivating future interconnects: a differential measurement analysis of PCI latency
Found in: Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS '09)
By Andrew W. Moore, David J. Miller, Philip M. Watts
Issue Date:October 2010
pp. 94-103
Local interconnect architectures are at a cusp in which advances in throughput have come at the expense of power and latency. Moreover, physical limits imposed on dissipation and packaging mean that further advances will require a new approach to interconn...
Fast nearest-neighbor search in disk-resident graphs
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Andrew W. Moore, Purnamrita Sarkar
Issue Date:July 2010
pp. 513-522
Link prediction, personalized graph search, fraud detection, and many such graph mining problems revolve around the computation of the most "similar" k nodes to a given query node. One widely used class of similarity measures is based on random walks on gr...
Probabilistic graphical models for semi-supervised traffic classification
Found in: Proceedings of the 6th International Wireless Communications and Mobile Computing Conference (IWCMC '10)
By Andrew W. Moore, Charalampos Rotsos, Jurgen Van Gael, Zoubin Ghahramani
Issue Date:June 2010
pp. 752-757
Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity in...
Fast dynamic reranking in large graphs
Found in: Proceedings of the 18th international conference on World wide web (WWW '09)
By Andrew W. Moore, Purnamrita Sarkar
Issue Date:April 2009
pp. 66-66
In this paper we consider the problem of re-ranking search results by incorporating user feedback. We present a graph theoretic measure for discriminating irrelevant results from relevant results using a few labeled examples provided by the user. The key i...
Fast incremental proximity search in large graphs
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By Amit Prakash, Andrew W. Moore, Purnamrita Sarkar
Issue Date:July 2008
pp. 896-903
In this paper we investigate two aspects of ranking problems on large graphs. First, we augment the deterministic pruning algorithm in Sarkar and Moore (2007) with sampling techniques to compute approximately correct rankings with high probability under ra...
Lightweight application classification for network management
Found in: Proceedings of the 2007 SIGCOMM workshop on Internet network management (INM '07)
By Andrew W. Moore
Issue Date:August 2007
pp. 299-304
Traffic application classification is an essential step in the network management process to provide high availability of network services. However, network management has seen limited use of traffic classification because of the significant overheads of e...
Learning for accurate classification of real-time traffic
Found in: Proceedings of the 2006 ACM CoNEXT conference (CoNEXT '06)
By Andrew W Moore, Wei Li
Issue Date:December 2006
pp. 89-97
Accurate network traffic classification is an important task. We intend to develop an intelligent classification system by learning the types of service inside a network flow using machine learning techniques. Previous work used Bayesian methods for traffi...
Sequential update of ADtrees
Found in: Proceedings of the 23rd international conference on Machine learning (ICML '06)
By Andrew W. Moore, Josep Roure
Issue Date:June 2006
pp. 769-776
Ingcreasingly, data-mining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database attributes are symbolic, ADtrees have already shown to be efficient structures t...
Fast inference and learning in large-state-space HMMs
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Andrew W. Moore, Sajid M. Siddiqi
Issue Date:August 2005
pp. 800-807
For Hidden Markov Models (HMMs) with fully connected transition models, the three fundamental problems of evaluating the likelihood of an observation sequence, estimating an optimal state sequence for the observations, and learning the model parameters, al...
Detection of emerging space-time clusters
Found in: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (KDD '05)
By Andrew W. Moore, Daniel B. Neill, Kenny Daniel, Maheshkumar Sabhnani
Issue Date:August 2005
pp. 218-227
We propose a new class of spatio-temporal cluster detection methods designed for the rapid detection of emerging space-time clusters. We focus on the motivating application of prospective disease surveillance: detecting space-time clusters of disease cases...
Bayes net graphs to understand co-authorship networks?
Found in: Proceedings of the 3rd international workshop on Link discovery (LinkKDD '05)
By Andrew W. Moore, Anna Goldenberg
Issue Date:August 2005
pp. 1-8
Improvements in data collection and the birth of online communities made it possible to obtain very large social networks (graphs). Several communities have been involved in modeling and analyzing these graphs. Usage of graphical models, such as Bayesian N...
Internet traffic classification using bayesian analysis techniques
Found in: Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems (SIGMETRICS '05)
By Andrew W. Moore, Denis Zuev
Issue Date:June 2005
pp. 50-60
Accurate traffic classification is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service to providing operators with useful forecasts for long-term provisioning. We apply a Na...
The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data
Found in: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04)
By Andrew W. Moore, Ke Yang, Ting Liu
Issue Date:August 2004
pp. 629-634
This paper is about a variant of k nearest neighbor classification on large many-class high dimensional datasets.K nearest neighbor remains a popular classification technique, especially in areas such as computer vision, drug activity prediction and astrop...
Rapid detection of significant spatial clusters
Found in: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04)
By Andrew W. Moore, Daniel B. Neill
Issue Date:August 2004
pp. 256-265
Given an N x N grid of squares, where each square has a count cij and an underlying population pij, our goal is to find the rectangular region with the highest density, and to calculate its significance by randomization. An arbitrary density function D, de...