Can User-Level Probing Detect and Diagnose Common Home-WLAN Pathologies?

Common WLAN pathologies include low signal-to-noise ratio, congestion, hidden terminals or interference from non-802.11 devices and phenomena. Prior work has focused on the detection and diagnosis of such problems using layer-2 information from 802.11 devices and special-purpose access points and monitors, which may not be generally available. Here, we investigate a userlevel approach: is it possible to detect and diagnose 802.11 pathologies with strictly user-level active probing, without any cooperation from, and without any visibility in, layer-2 devices? In this paper, we present preliminary but promising results indicating that such diagnostics are feasible

End-to-end inference of internet performance problems

Inference, measurement and estimation of network path properties is a fundamental problem in distributed systems and networking. We consider a specific subclass of problems which do not require special support from the hardware or software, deployment of special devices or data from the network. Network inference is a challenging problem since Internet paths can have complex and heterogeneous configurations. Inference enables end users to understand and troubleshoot their connectivity and verify their service agreements; it has policy implications from network neutrality to broadband performance; and it empowers applications and services to adapt to network paths to improve user quality of experience. In this dissertation we develop end-to-end user-level methods, tools and services for network inference. Our contributions are as follows. We show that domain knowledge-based methods can be used to infer performance of different types of networks, containing wired and wireless links, and ranging from local area to inter-domain networks. We develop methods to infer network properties: 1. Traffic discrimination (DiffProbe), 2. Traffic shapers and policers (ShaperProbe), and 3. Shared links among multiple paths (Spectral Probing). We develop methods to understand network performance: 1. Diagnose wireless performance pathologies (WLAN-probe), and 2. Diagnose wide-area performance pathologies (Pythia). Among our contributions: We have provided ShaperProbe as a public service and it has received over 1.5 million runs from residential and commercial users, and is used to check service level agreements by thousands of residential broadband users a day. The Federal Communications Commission (FCC) has recognized DiffProbe and ShaperProbe with the best research award in the Open Internet Apps Challenge in 2011. We have written an open source performance diagnosis system, Pythia, and it is being deployed in ISPs such as the US Department of Energy ESnet in wide-area inter-domain settings. The contributions of this dissertation enable Internet transparency, performance troubleshooting and improving distributed systems performance.PhDCommittee Chair: Dovrolis, Constantine; Committee Member: Ammar, Mostafa; Committee Member: Claffy, Kimberly; Committee Member: Papagiannaki, Konstantina; Committee Member: Zegura, Elle

DiffProbe: Detecting ISP service discrimination

Abstract—We propose an active probing method, called Differential Probing or DiffProbe, to detect whether an access ISP is deploying forwarding mechanisms such as priority scheduling, variations of WFQ, or WRED to discriminate against some of its customer flows. DiffProbe aims to detect if the ISP is doing one or both of delay discrimination and loss discrimination. The basic idea in DiffProbe is to compare the delays and packet losses experienced by two flows: an Application flow A and a Probing flow P. The paper describes the statistical methods that DiffProbe uses, a novel method for distinguishing between Strict Priority and WFQ-variant packet scheduling, simulation and emulation experiments, and a few real-world tests at major access ISPs. I

End-to-end Detection of ISP Traffic Shaping using Active and Passive Methods

We present an end-to-end measurement method for the detection of traffic shaping. Traffic shaping is typically implemented using token buckets, allowing a maximum burst of traffic to be serviced at the peak capacity of the link, while any remaining traffic is serviced at a lower shaping rate. The contribution of this paper is twofold. First, we develop an active end-to-end detection mechanism, referred to as Shaper-Probe, that can infer whether a particular path is subject to traffic shaping, and in that case, estimate the shaper characteristics. Second, we analyze results from a large-scale deployment of ShaperProbe on M-Lab over the last 24 months, detecting traffic shaping in several major ISPs. Our deployment has received more than one million runs so far from 5,700 ISPs. 1

Spectral probing, crosstalk and frequency multiplexing in internet paths

We present an end-to-end active probing methodology that creates frequency-domain signals in IP network paths. The signals are generated by periodic packet trains that cause short-lived queueing delay spikes. Different probers can be multiplexed in the frequency-domain on the same path. Further, a signal that is introduced by a “prober ” in one path can cause a crosstalk effect, inducing a signal of the same frequency into another path (the “sampler”) as long as the two paths share one or more bottleneck queues. Applications of the proposed methodology include the detection of shared store-and-forward devices among two or more paths, the creation of covert channels, and the modulation of voice or video periodic packet streams in less noisy frequencies. In this paper we focus on the first application. Our goal is to detect shared bottleneck(s) between a “sampler ” and one or more “prober ” paths. We present a spectral probing methodology as well as the corresponding signal processing/detection process. The accuracy of the method has been evaluated with controlled and repeatable simulation experiments, and it has also been tested on some Internet paths