6,548 research outputs found
Study of the CAC mechanisms for telecommunications systems with adaptive links according to propagation conditions
This paper presents the framework and the activities of a PhD research work in progress supported by Alcatel Alenia Space in collaboration with TeSA and SUPAERO. It deals with Connection Admission Control (CAC) for Telecommunications Systems with adaptive links according to propagation conditions. Indeed, in high frequency bands communications, deep fadings may occur because of atmospheric propagation losses. The mitigation techniques used to counteract fades impacts the system capacity, therefore the CAC mechanism. The CAC which only uses current capacity information may lead to intolerable dropping of admitted connection, and thus breaches the QoS guarantees made upon connection acceptance. New CAC mechanisms shall be studied to take into account the capacity variation and the mitigation techniques (IFMT) developed to compensate the attenuation in Ka and above frequency range
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
Knowing the largest rate at which data can be sent on an end-to-end path such
that the egress rate is equal to the ingress rate with high probability can be
very practical when choosing transmission rates in video streaming or selecting
peers in peer-to-peer applications. We introduce probabilistic available
bandwidth, which is defined in terms of ingress rates and egress rates of
traffic on a path, rather than in terms of capacity and utilization of the
constituent links of the path like the standard available bandwidth metric. In
this paper, we describe a distributed algorithm, based on a probabilistic
graphical model and Bayesian active learning, for simultaneously estimating the
probabilistic available bandwidth of multiple paths through a network. Our
procedure exploits the fact that each packet train provides information not
only about the path it traverses, but also about any path that shares a link
with the monitored path. Simulations and PlanetLab experiments indicate that
this process can dramatically reduce the number of probes required to generate
accurate estimates
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
Performance of TCP with multiple Priority Classes
We consider the dimensioning problem for Internet access links carrying TCP traffic with two priority classes. To this end, we study the behaviour of TCP at the flow level described by a multiple-server Processor Sharing (PS) queueing model with two customer classes, where the customers represent flows generated by downloading Internet objects; the sojourn times represent the object transfer times. We present closed-form expressions for the mean sojourn times for high-priority customers and approximate expressions for the mean sojourn times of low-priority customers. The accuracy of the model is demonstrated by comparing results based on the PS model with "real" TCP simulation results obtained by the well-known Network Simulator. The experimental results demonstrate that the model-based results are highly accurate when the mean object size is at least 10 IP-packets, and the loss rate is negligible
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