The effectiveness of rate adaptation algorithms is an important determinant of 802.11 wireless network performance. The diversity of algorithms that has resulted from efforts to improve rate adaptation has introduced a new dimension of variability into 802.11 wireless networks, further complicating the already difficult task of understanding and debugging 802.11 performance. To assist with this task, in this paper we present and evaluate a methodology for accurately fingerprinting 802.11 rate adaptation algorithms. Our approach uses a Support Vector Machine (SVM)-based classifier that requires only simple passive measurements of 802.11 traffic. We demonstrate that careful conversion of raw packet traces into input features for SVM is necessary for achieving high classification accuracy. We tested our classifier on the four rate adaptation algorithms available in MadWifi, cards. The classifier performs with an accuracy of 95% – 100%. We also show that the classifier is robust over a variety of network conditions if the training data includes a sufficient sampling of the range of an algorithm's behavior.

TCP throughput predictionis an important capability for networks where multiple paths exist between data senders and receivers. In this paper, we describe a new lightweight method for TCP throughput prediction. Our predictor uses Support Vector Regression (SVR); prediction is based on both prior file transfer history and measurements of simple path properties. We evaluate our predictor in a laboratory setting where ground truth can be measured with perfect accuracy. We report the performance of our predictor fororacularandpracticalmeasurements of path properties over a wide range of traffic conditions and transfer sizes. For bulk transfers in heavy traffic usingoracularmeasurements, TCP throughput is predicted within 10% of the actual value 87% of the time, representing nearly a threefold improvement in accuracy over prior history-based methods. Forpracticalmeasurements of path properties, predictions can be made within 10% of the actual value nearly 50% of the time, approximately a 60% improvement over history-based methods, and with much lower measurement traffic overhead. We implement our predictor in a tool calledPathPerf, test it in the wide area, and show thatPathPerfpredicts TCP throughput accurately over diverse wide area paths.

The increasing density of WiFi access points (APs) in metropolitan areas is enabling an opportunistic model of wireless networking, whereby a "guest" user within range of one or more wireless APs can gain temporary Internet access through these APs. In this paper, we address the problem of TCP throughput prediction for opportunistic networks. Applications of opportunistic networking can benefit from such predictions by adapting to prevailing network conditions. Our approach is different from prior efforts to model wireless network throughput in that only the two communicating endpoints participate in the prediction, and no information about network topology or traffic loads generated by interfering sources is required. Our goal is to understand how accurate throughput predictions can be under the above assumptions. The physical environment considered in our study includes varying degrees of interference, indoor and outdoor networks, and nodes that are stationary or moving at walking or driving speeds. We use throughput predictors based on time series analysis and machine learning techniques, as they are well-suited to predicting phenomena with unknown variables. The prediction accuracy that our methods yield is cause for cautious optimism. We find that 80% to 100% of predictions are within a factor of two of actual throughput. This bound on accuracy means that predictions are useful for certain applications, because this bound (a) can be achieved by measurements lasting for as little as 0.3 seconds, and (b) holds even when nodes are driving at speeds of 15-25 mph.

TCP throughput prediction is an important capability in wide area overlay and multi-homed networks where multiple paths may exist between data sources and receivers. In this paper we describe a new, lightweight method for TCP throughput prediction that can generate accurate forecasts for a broad range of file sizes and path conditions. Our method is based on Support Vector Regression modeling that uses a combination of prior file transfers and measurements of simple path properties. We calibrate and evaluate the capabilities of our throughput predictor in an extensive set of lab-based experiments where ground truth can be established for path properties using highly accurate passive measurements. We report the performance for our method in the ideal case of using our passive path property measurements over a range of test configurations. Our results show that for bulk transfers in heavy traffic, TCP throughput is predicted within 10% of the actual value 87% of the time, representing nearly a 3-fold improvement in accuracy over prior history-based methods. In the same lab environment, we assess our method using less accurate active probe measurements of path properties, and show that predictions can be made within 10% of the actual value nearly 50% of the time over a range of file sizes and traffic conditions. This result represents approximately a 60% improvement over history-based methods with a much lower impact on end-to-end paths. Finally, we implement our predictor in a tool called PathPerf and test it in experiments conducted on wide area paths. The results demonstrate that PathPerf predicts TCP through put accurately over a variety of paths.