Improving TCP/IP performance over wireless networks
MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
A comparison of mechanisms for improving TCP performance over wireless links
IEEE/ACM Transactions on Networking (TON)
Hidden Markov modeling for network communication channels
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Effectiveness of Loss Labeling in Improving TCP Performance in Wired/Wireless Networks
ICNP '02 Proceedings of the 10th IEEE International Conference on Network Protocols
High Performance Wide Area Data Transfers over High Performance Networks
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Discriminating Congestion Losses from Wireless Losses using Inter-Arrival Times at the Receiver
ASSET '99 Proceedings of the 1999 IEEE Symposium on Application - Specific Systems and Software Engineering and Technology
An Evaluation of Object-Based Data Transfers on High Performance Networks
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
MSS '01 Proceedings of the Eighteenth IEEE Symposium on Mass Storage Systems and Technologies
Performance of TCP Congestion Predictors as Loss Predictors
Performance of TCP Congestion Predictors as Loss Predictors
Classifiers for the causes of data loss using packet-loss signatures
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
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Given the critical nature of communications in computational Grids it is important to develop efficient, intelligent, and adaptive communication mechanisms. An important milestone on this path is the development of classification mechanisms that can distinguish between the various causes of data loss in cluster and Grid environments. The idea is to use the classification mechanism to determine if data loss is caused by contention within the network or if the cause lies outside of the network domain. If it is outside of the network domain, then it is not necessary to trigger aggressive congestion-control mechanisms. Thus the goal is to operate the data transfer at the highest possible rate by only backing off aggressively when the data loss is classified as being network related. In this paper, we investigate one promising approach to developing such classification mechanisms based on the analysis of the patterns of packet loss and the application of Bayesian statistics.