4,468 research outputs found
ABC: A Simple Explicit Congestion Controller for Wireless Networks
We propose Accel-Brake Control (ABC), a simple and deployable explicit
congestion control protocol for network paths with time-varying wireless links.
ABC routers mark each packet with an "accelerate" or "brake", which causes
senders to slightly increase or decrease their congestion windows. Routers use
this feedback to quickly guide senders towards a desired target rate. ABC
requires no changes to header formats or user devices, but achieves better
performance than XCP. ABC is also incrementally deployable; it operates
correctly when the bottleneck is a non-ABC router, and can coexist with non-ABC
traffic sharing the same bottleneck link. We evaluate ABC using a Wi-Fi
implementation and trace-driven emulation of cellular links. ABC achieves
30-40% higher throughput than Cubic+Codel for similar delays, and 2.2X lower
delays than BBR on a Wi-Fi path. On cellular network paths, ABC achieves 50%
higher throughput than Cubic+Codel
Forecasting international bandwidth capacity using linear and ANN methods
An artificial neural network (ANN) can improve forecasts through pattern recognition of historical data. This article evaluates the reliability of ANN methods, as opposed to simple extrapolation techniques, to forecast Internet bandwidth index data that is bursty in nature. A simple feedforward ANN model is selected as a nonlinear alternative, as it is flexible enough to model complex linear or nonlinear relationships without any prior assumptions about the data generating process. These data are virtually white noise and provides a challenge to forecasters. Using standard forecast error statistics, the ANN and the simple exponential smoothing model provide modestly better forecasts than other extrapolation methodsForecasting; international bandwidth capacity
An Online Decision-Theoretic Pipeline for Responder Dispatch
The problem of dispatching emergency responders to service traffic accidents,
fire, distress calls and crimes plagues urban areas across the globe. While
such problems have been extensively looked at, most approaches are offline.
Such methodologies fail to capture the dynamically changing environments under
which critical emergency response occurs, and therefore, fail to be implemented
in practice. Any holistic approach towards creating a pipeline for effective
emergency response must also look at other challenges that it subsumes -
predicting when and where incidents happen and understanding the changing
environmental dynamics. We describe a system that collectively deals with all
these problems in an online manner, meaning that the models get updated with
streaming data sources. We highlight why such an approach is crucial to the
effectiveness of emergency response, and present an algorithmic framework that
can compute promising actions for a given decision-theoretic model for
responder dispatch. We argue that carefully crafted heuristic measures can
balance the trade-off between computational time and the quality of solutions
achieved and highlight why such an approach is more scalable and tractable than
traditional approaches. We also present an online mechanism for incident
prediction, as well as an approach based on recurrent neural networks for
learning and predicting environmental features that affect responder dispatch.
We compare our methodology with prior state-of-the-art and existing dispatch
strategies in the field, which show that our approach results in a reduction in
response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201
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