Application, Network and Link Layer Measurements of Streaming Video over a Wireless Campus Network

Li, Feng; Chung, Jae; Li, Mingzhe; Wu, Huahui; Claypool, Mark; Kinicki, Robert

doi:10.1007/978-3-540-31966-5_15

Cited by 13 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we use a wireless network performance dataset 21 as an example. It is from the Wireless Multimedia Streaming Laboratory in the Computer Science Department at WPI, and describes the measurements of the physical, network and application layers of a streaming video over a wireless campus network.…”

Section: Case Study 1: Time Series Data Analysismentioning

confidence: 99%

Enhancing scatterplot matrices for data with ordering or spatial attributes

Cui

Ward

Rundensteiner

2006

Visualization and Data Analysis 2006

View full text Add to dashboard Cite

The scatterplot matrix is one of the most common methods used to project multivariate data onto two dimensions for display. While each off-diagonal plot maps a pair of non-identical dimensions, there is no prescribed mapping for the diagonal plots. In this paper, histograms, 1D plots and 2D plots are drawn in the diagonal plots of the scatterplots matrix. In 1D plots, the data are assumed to have order, and they are projected in this order. In 2D plots, the data are assumed to have spatial information, and they are projected onto locations based on these spatial attributes using color to represent the dimension value. The plots and the scatterplots are linked together by brushing. Brushing on these alternate visualizations will affect the selected data in the regular scatterplots, and vice versa. Users can also navigate to other visualizations, such as parallel coordinates and glyphs, which are also linked with the scatterplot matrix by brushing. Ordering and spatial attributes can also be used as methods of indexing and organizing data. Users can select an ordering span or a spatial region by interacting with 1D plots or with 2D plots, and then observe the characteristics of the selected data subset. 1D plots and 2D plots provide the ability to explore the ordering and spatial attributes, while other views are for viewing the abstract data. In a sense, we are linking what are traditionally seen as scientific visualization methods with methods from the information visualization and statistical graphics fields. We validate the usefulness of this integration by providing two case studies, time series data analysis and spatial data analysis.

show abstract

Section: Case Study 1: Time Series Data Analysismentioning

confidence: 99%

Enhancing scatterplot matrices for data with ordering or spatial attributes

Cui

Ward

Rundensteiner

2006

Visualization and Data Analysis 2006

View full text Add to dashboard Cite

show abstract

“…For this investigation, several previously-developed [13] measurement tools for collecting data at multiple network layers were installed on two laptops. Table 1 lists the tools employed in this study and provides examples of the performance metrics available from each tool.…”

Section: Toolsmentioning

confidence: 99%

“…In the second layout, host A remains at its good location while host B is placed at a location with bad wireless connectivity. Location identification and classification come from previous experiments [13] such that a good location has an average SignalStrength ≥ −70dBm and a bad location has an average SignalStrength ≤ −75dBm.…”

Section: Experimental Designmentioning

confidence: 99%

“…However, the results thus far focus only on analyzing [10] and measuring performance at the wireless MAC layer [2], and thus they only provide meaningful results under narrow conditions. Previous work [13] indicates that performance aspects of the link layer, network layer and application layer can be inter-layer related. This suggests that effective models of infrastructure WLAN performance need to be aware of interactions between the network layers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance analysis of the intertwined effects between network layers for 802.11g transmissions

Gretarsson

Feng

et al. 2005

Proceedings of the 1st ACM Workshop on Wireless Multimedia Networking and Performance Modeling

Self Cite

View full text Add to dashboard Cite

While the canonical behavior of today's home Internet users involves several residents concurrently executing diverse Internet applications, the most common home configuration is a single external connection into a wireless access point (AP) that promises to provide concurrent high-bandwidth Internet access for multiple hosts through a wireless local area network (WLAN). Recent research has attempted to assess the performance impact of hosts with weak wireless connectivity upon the other WLAN hosts by employing measurement studies or analytic models that focus primarily on wireless channel characteristics. This paper examines the intertwined effects on performance of the user applications, the network protocol and the wireless channel characteristics via carefully designed experiments that leverage previously developed network measurement tools. The study provides empirical evidence that suggests the overall performance of a WLAN is not only determined by the individual wireless channel qualities associated with each host, but also by the interaction of the various network layers with respect to transmission contention, queuing at the access point, transport protocol, and behavior of the specific applications. These results imply that effective WLAN performance modeling needs to include details on multiple network layers.

show abstract

“…Multimedia servers like Windows Streaming Media use the packet pair technique to estimate the bottleneck capacity from the server to the client [12]. Nevertheless, results presented in [13] show that the accuracy of the method is not good when the clients are connected to a WLAN.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Technique to Estimate the Transmission Rate of an IEEE 802.11 WLAN

Rocha

Leão

Silva

2007

2007 IEEE International Conference on Communications

View full text Add to dashboard Cite

Abstract-The deployment of wireless LANs (WLANs) has been steadily increasing over the years and estimating the actual bit rate of a WLAN device is important for management and applications such as those that can adapt their transmission rates according to the network characteristics. We propose a simple and accurate active measurement technique to infer the bit rate of an IEEE802.11 device. The proposed method is based both on a recently proposed technique to infer the type of access network and on the packet pair approach, but adapted to take into account the overhead caused by the IEEE802.11 control and the existence of concurrent WLAN traffic. Furthermore, the technique does not require the WLAN to be the bottleneck link in the path from the measuring point to the end computer. Results from simulation and from measurements show that the approach is accurate to infer WLAN access point rates both in scenarios where the WLAN devices can adapt their rates as well as WLANs with fixed transmission rates. I. INTRODUCTIONWireless local area networks (WLANs) based on the IEEE 802.11 standards [1] have become one of the most popular access networks. The high transmission rates offered by the current standards (802.11a/b/g) and the significant reduction in the costs of the equipment have greatly eased the deployment of this technology on public locations (e.g. airports, universities, libraries, cafes, shopping centers) and residences.Access networks consist of the links between end systems and edge Internet routers. Examples of such links are dial-up lines, ADSL, Cable Modem, Ethernet and WLAN. The various types of access networks have different characteristics in terms of transmissions speeds, physical media, and capacity in the reverse and forward links.Identifying the connection type of the last hop of an Internet path can be very useful for several applications, such as video streaming, overlay networks and peer to peer. For example, in order to improve performance, it may be advantageous to peerto-peer applications to know the type of access network (as suggested in [2]) and the transmission rates of the clients, in order to choose the best peer to connect. As another example, there are proposals to improve TCP performance when the last hop is a WLAN [3] or a cable modem [4].These examples explain why recent research was devoted to characterizing the type of connection available to an end user. The work of [5] was the first aiming at differentiating between wireless and wired network access. It is based on the observation of round trip times (RTT's) of TCP connections. In [2] the authors proposed a method to identify the type of an access network (Ethernet, WLAN, or low bandwidth).

show abstract

Application, Network and Link Layer Measurements of Streaming Video over a Wireless Campus Network

Cited by 13 publications

References 9 publications

Enhancing scatterplot matrices for data with ordering or spatial attributes

Enhancing scatterplot matrices for data with ordering or spatial attributes

Performance analysis of the intertwined effects between network layers for 802.11g transmissions

An End-to-End Technique to Estimate the Transmission Rate of an IEEE 802.11 WLAN

Contact Info

Product

Resources

About