Addressing the Influence of Hidden State on Wireless Network Optimizations Using Performance Maps
Performance of wireless connectivity for network client devices is location dependent. It has been shown that it can be beneficial to collect network performance metrics along with location information to generate maps of the location dependent network performance. These performance maps can be used to optimize wireless networks by predicting future network performance and scheduling the network communication for certain applications on mobile devices. However, other important factors influence the performance of the wireless communication such as changes in the propagation environment and resource sharing. In this work we extend the framework of performance maps for wireless networks by introducing network state as an abstraction for all other factors than location that influence the performance. Since network state might not always be directly observable the framework is extended with a network state estimation and prediction function. In the evaluation scenario, resource sharing is used as an example of network state and the framework is applied to the use-case of scheduling TCP-based communication to lower communication overhead. Using extensive simulations for evaluation we show how dynamic network state caused by resource sharing influences the scheduling performance.
KeywordsWireless Network Mobile Node Network Performance Network State Transmission Probability
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- 1.Yao, J., Kanhere, S., Hassan, M.: Improving QoS in High-speed Mobility Using Bandwidth Maps. IEEE Transactions on Mobile Computing 10(11) (2011)Google Scholar
- 2.Schulman, A., Navda, V., Ramjee, R., Spring, N., Deshpande, P., Grunewald, C., Jain, K., Padmanabhan, V.: Bartendr: a practical approach to energy-aware cellular data scheduling. In: Proceedings of MobiCom 2010, pp. 85–96 (2010). doi: 10.1145/1859995.1860006
- 3.Nicholson, A., Noble, B.: BreadCrumbs: forecasting mobile connectivity. In: Proceedings of MobiCom 2008, pp. 46–57. doi: 10.1145/1409944.1409952
- 4.Riiser, H., Endestad, T., Vigmostad, P., Griwodz, C., Halvorson, P.: Video streaming using a location-based bandwidth-lookup service for bitrate planning. ACM TOMCCAP 8(3) (2012). doi: 10.1145/2240136.2240137
- 5.Højgaard-Hansen, K., Madsen, T., Schwefel, H.P.: Reducing communication overhead by scheduling TCP transfers on mobile devices using wireless network performance maps. In: Proceedings of European Wireless 2012 (2012)Google Scholar
- 6.Padhye, J., Firoiu, V., Towsley, D., Kurose, J.: Modeling TCP throughput: a simple model and its empirical validation. SIGCOMM Comput. Commun. Rev., 28(4) (1998). doi: 10.1145/285243.285291
- 7.Cardwell, N., Savage, S., Anderson, T.: Modeling TCP latency. In: Proceedings of IEEE INFOCOM 2000 (2000). doi: 10.1109/INFCOM.2000.832574
- 8.Nielsen, J.J., Madsen, T., Schwefel, H.P.: Location assisted handover optimization for heterogeneous wireless networks. In: Proceedings of European Wireless 2011 (2011)Google Scholar
- 10.Wireshark Display Filter Reference - TCP. https://www.wireshark.org/docs/dfref/t/tcp.html