42,397 research outputs found
SSthreshless Start: A Sender-Side TCP Intelligence for Long Fat Network
Measurement shows that 85% of TCP flows in the internet are short-lived flows
that stay most of their operation in the TCP startup phase. However, many
previous studies indicate that the traditional TCP Slow Start algorithm does
not perform well, especially in long fat networks. Two obvious problems are
known to impact the Slow Start performance, which are the blind initial setting
of the Slow Start threshold and the aggressive increase of the probing rate
during the startup phase regardless of the buffer sizes along the path. Current
efforts focusing on tuning the Slow Start threshold and/or probing rate during
the startup phase have not been considered very effective, which has prompted
an investigation with a different approach. In this paper, we present a novel
TCP startup method, called threshold-less slow start or SSthreshless Start,
which does not need the Slow Start threshold to operate. Instead, SSthreshless
Start uses the backlog status at bottleneck buffer to adaptively adjust probing
rate which allows better seizing of the available bandwidth. Comparing to the
traditional and other major modified startup methods, our simulation results
show that SSthreshless Start achieves significant performance improvement
during the startup phase. Moreover, SSthreshless Start scales well with a wide
range of buffer size, propagation delay and network bandwidth. Besides, it
shows excellent friendliness when operating simultaneously with the currently
popular TCP NewReno connections.Comment: 25 pages, 10 figures, 7 table
A two-level Markov model for packet loss in UDP/IP-based real-time video applications targeting residential users
The packet loss characteristics of Internet paths that include residential broadband links are not well understood, and there are no good models for their behaviour. This compli- cates the design of real-time video applications targeting home users, since it is difficult to choose appropriate error correction and concealment algorithms without a good model for the types of loss observed. Using measurements of residential broadband networks in the UK and Finland, we show that existing models for packet loss, such as the Gilbert model and simple hidden Markov models, do not effectively model the loss patterns seen in this environment. We present a new two-level Markov model for packet loss that can more accurately describe the characteristics of these links, and quantify the effectiveness of this model. We demonstrate that our new packet loss model allows for improved application design, by using it to model the performance of forward error correction on such links
Large scale probabilistic available bandwidth estimation
The common utilization-based definition of available bandwidth and many of
the existing tools to estimate it suffer from several important weaknesses: i)
most tools report a point estimate of average available bandwidth over a
measurement interval and do not provide a confidence interval; ii) the commonly
adopted models used to relate the available bandwidth metric to the measured
data are invalid in almost all practical scenarios; iii) existing tools do not
scale well and are not suited to the task of multi-path estimation in
large-scale networks; iv) almost all tools use ad-hoc techniques to address
measurement noise; and v) tools do not provide enough flexibility in terms of
accuracy, overhead, latency and reliability to adapt to the requirements of
various applications. In this paper we propose a new definition for available
bandwidth and a novel framework that addresses these issues. We define
probabilistic available bandwidth (PAB) as the largest input rate at which we
can send a traffic flow along a path while achieving, with specified
probability, an output rate that is almost as large as the input rate. PAB is
expressed directly in terms of the measurable output rate and includes
adjustable parameters that allow the user to adapt to different application
requirements. Our probabilistic framework to estimate network-wide
probabilistic available bandwidth is based on packet trains, Bayesian
inference, factor graphs and active sampling. We deploy our tool on the
PlanetLab network and our results show that we can obtain accurate estimates
with a much smaller measurement overhead compared to existing approaches.Comment: Submitted to Computer Network
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