Throughput Evaluation for Improving
Predictability over the Internet
Cybermedia Center, Osaka University, Japan
Experimental settings

Applications in Grid environment are required to share distributed
large blocks of data among distributed multiple sites.
Network throughput for data transfer affects total processing time
as well as the task processing.

We used PlanetLab nodes, equipped with a virtualization
mechanism called V-Server (http://www.linux-vserver.org).
Various sizes for both probe and data transfer are used.
Correlation between both probes is evaluated by Spearman’ srank correlation coefficient, one of non-parametric metrics.

The predictednetwork throughput
would be a usefulparameteron scheduling tasks
to improve the total
processingperformance.

Original result
Smaller-size probes had better conditions than larger-sizeprobes.

Purpose of our research
Improving precision of the throughput prediction method called“Network Weather Service” [1].
Adapting the prediction method to virtualized hosting environment,
which shows anomalous behavior more
frequently thanphysical nodes.

Connection pair

Results without anomalous cases
We re-evaluate the results without the anomalies, and alarger-size probe is required for improving predictability.
If throughput is decreased by the anomalies, we should carefully
review measurement results.

A network throughput prediction has been a challenging issue
due to the dynamics of network traffic and no guarantee forbandwidth reservation.
Connection pair uses a small size of probe transfer to predict thethroughput of a large size of data transfer.
In previous work [1], the restricted sets of pairs on probe anddata size were examined on limited network environment.

Conclusion and future work

Traffic anomalies
The evaluation results were affected by oversize packetspacings, which are caused by CPU scheduling latency.
The packet spacing which is larger than the TCP transmission
period involves packet transmissions, which results in severe
throughput[2].

Anomalies from virtualized hosting environment have great
impacts on the prediction results.
We re-evaluate the results without the anomalies, and found thatlarger-size probe is required for improving predictability.
Our future works are to measure throughput with various probe
size and to devise an anomaly estimation method.

References[1] M. Swany and R. Wolski, Multivariate Resource Performance
Forecasting in the Network Weather Service, in Proceedings ofIEEE/ACM Conference on High-Performance Computing andNetworking, pp 1-10, November 2002,.
[2] C. Lee, H. Abe, T. Hirotsu, and kyoji Umemura, “Analysis ofanomalies on a virtualized network testbed,” in Proceedings of the10th IEEE International Conference on Computer and Information
Technology, pp. 297–304, June 2010.

Chunghan Lee：Toyohashi University of Tech.Dept. of Electronic and Information Eng.
Hirotake Abe：Osaka University Cybermedia Center
Toshio Hirotsu ：Hosei University Faculty of Computer and Information Sciences
Kyoji Umemura ：Toyohashi University of Tech.Dept. of Computer Science and Eng.

Contact: Hirotake Abe
E-mail: habe@cmc.osaka-u.ac.jp

Throughput Evaluation for Improving Predictability over the Internet

Motivation