Artificial Intelligence Review - Special issue on lazy learning
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Benchmarking with Real Industrial Applications: The SPEC High-Performance Group
IEEE Computational Science & Engineering
Using Text Categorization Techniques for Intrusion Detection
Proceedings of the 11th USENIX Security Symposium
Learning Program Behavior Profiles for Intrusion Detection
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Scalable analysis techniques for microprocessor performance counter metrics
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Policy Driven Heterogeneous Resource Co-Allocation with Gangmatching
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
RUMR: Robust Scheduling for Divisible Workloads
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
A Case For Grid Computing On Virtual Machines
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using Active Learning in Intrusion Detection
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
VMPlants: Providing and Managing Virtual Machine Execution Environments for Grid Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Improving Goodput by Coscheduling CPU and Network Capacity
International Journal of High Performance Computing Applications
Skeleton based performance prediction on shared networks
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
From virtualized resources to virtual computing grids: the In-VIGO system
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Training a neural-network based intrusion detector to recognize novel attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned
Expert Systems with Applications: An International Journal
Classifying execution times in parallel computing systems: a classical hypothesis testing approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Black box scheduling for resource intensive virtual machine workloads with interference models
Future Generation Computer Systems
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Application awareness is an important factor of efficient resource scheduling. This paper introduces a novel approach for application classification based on the Principal Component Analysis (PCA) and the k-Nearest Neighbor (k-NN) classifier. This approach is used to assist scheduling in heterogeneous computing environments. It helps to reduce the dimensionality of the performance feature space and classify applications based on extracted features. The classification considers four dimensions: CPU-intensive, I/O and paging-intensive, network-intensive, and idle. Application class information and the statistical abstracts of the application behavior are learned over historical runs and used to assist multi-dimensional resource scheduling. This paper describes a prototype classifier for applicationcentric Virtual Machines. Experimental results show that scheduling decisions made with the assistance of the application class information, improved system throughput by 22.11% on average, for a set of three benchmark applications.