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.
Increasingly online reviews are relied upon to make choices about the purchases and services we use daily. Businesses, on the other hand, depend on online review sites to find new customers and understand people's perception of them. In order for an online review community to be effective to both users and businesses, it is important to understand what constitutes a high quality review as perceived by people, and how to maximize quality of reviews in the community. In this paper, we study Yelp to answer these questions. We analyze about 230,000 reviews and member interaction ("votes") with these reviews. We find that active and regular members are the highest contributors to good quality reviews and longer reviews have higher chances of being popular in the community. We find that reviews voted "useful" tend to be the early ones for a specific business. Our findings have implications on enabling high quality member contributions and community effectiveness. We discuss the implications to design of social systems with diverse feedback signals.
Common Wireless LAN (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 user-level 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.
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 ShaperProbe, 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.
Despite the growing popularity of video streaming over the Internet, problems such as re-buffering and high startup latency continue to plague users. In this paper, we present an end-to-end characterization of Yahoo's video streaming service, analyzing over 500 million video chunks downloaded over a two-week period. We gain unique visibility into the causes of performance degradation by instrumenting both the CDN server and the client player at the chunk level, while also collecting frequent snapshots of TCP variables from the server network stack. We uncover a range of performance issues, including an asynchronous disk-read timer and cache misses at the server, high latency and latency variability in the network, and buffering delays and dropped frames at the client. Looking across chunks in the same session, or destined to the same IP prefix, we see how some performance problems are relatively persistent, depending on the video's popularity, the distance between the client and server, and the client's operating system, browser, and Flash runtime.
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