The nature of statistical learning theory
The nature of statistical learning theory
Future Generation Computer Systems - Special issue on metacomputing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the characteristics and origins of internet flow rates
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
A pragmatic definition of elephants in internet backbone traffic
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Predicting the Performance of Wide Area Data Transfers
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
The End-to-End Performance Effects of Parallel TCP Sockets on a Lossy Wide-Area Network
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Multivariate resource performance forecasting in the network weather service
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Predicting Sporadic Grid Data Transfers
HPDC '02 Proceedings of the 11th 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
A tutorial on support vector regression
Statistics and Computing
Characterizing and Predicting TCP Throughput on the Wide Area Network
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
On the predictability of large transfer TCP throughput
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Neural Computation
SVM learning of IP address structure for latency prediction
Proceedings of the 2006 SIGCOMM workshop on Mining network data
A machine learning approach to TCP throughput prediction
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Heterogeneity-Aware Workload Distribution in Donation-Based Grids
International Journal of High Performance Computing Applications
An active measurement system for shared environments
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
An autonomic network-aware scheduling architecture for grid computing
Proceedings of the 5th international workshop on Middleware for grid computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
Fog in the network weather service: a case for novel approaches
Proceedings of the first international conference on Networks for grid applications
Support vector regression for link load prediction
Computer Networks: The International Journal of Computer and Telecommunications Networking
Using Data Mining Algorithms in Web Performance Prediction
Cybernetics and Systems
Enabling and optimizing pilot jobs using xen based virtual machines for the HPC grid applications
VTDC '09 Proceedings of the 3rd international workshop on Virtualization technologies in distributed computing
A study of a KVM-based cluster for grid computing
Proceedings of the 47th Annual Southeast Regional Conference
A data throughput prediction and optimization service for widely distributed many-task computing
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Peta-Flow Computing: Vision and Challenges
SAINT '11 Proceedings of the 2011 IEEE/IPSJ International Symposium on Applications and the Internet
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Grid applications are increasingly becoming dependent on network resources. Predicted network throughput is a useful parameter for network-aware scheduling for such applications. Although throughput prediction methods have been proposed, many of these methods are suffering from the fact that the probability distribution of traffic is unclear and the scale and bandwidth of networks are constantly changing. Furthermore, a virtual machine has been used as a platform for grid computing, and it can affect network measurement. A prediction method that uses pairs of differently sized connections has been proposed. This method, which we call connection pair, features a small probe transfer that predicts the throughput of a large data transfer. We propose a throughput prediction method based on the connection pair that uses v-support vector regression (SVR) and polynomial kernel to deal with prediction models represented as a non-linear and continuous monotonic function. The prediction accuracy of our method compared to that of a previous prediction method is higher. Moreover, the drop in the accuracy is also smaller than that of the previous method under an unstable network state. We clarify the prediction accuracy with other probe sizes for the connection pair. The accuracy is decreased by a small-sized probe, and there are no changes with a large-sized probe. These results show that our method is accurate, robust, and suitable for its purpose.