1,679 research outputs found
Heterogeneous Congestion Control: Efficiency, Fairness and Design
When heterogeneous congestion control protocols that react to different pricing signals (e.g. packet loss, queueing delay, ECN marking etc.) share the same network, the current theory based on utility maximization fails to predict the network behavior. Unlike in a homogeneous network, the bandwidth allocation now depends on router parameters and flow arrival patterns. It can be non-unique, inefficient and unfair. This paper has two objectives. First, we demonstrate the intricate behaviors of a heterogeneous network through simulations and present a rigorous framework to help understand its equilibrium efficiency and fairness properties. By identifying an optimization problem associated with every equilibrium, we show that every equilibrium is Pareto efficient and provide an upper bound on efficiency loss due to pricing heterogeneity. On fairness, we show that intra-protocol fairness is still decided by a utility maximization problem while inter-protocol fairness is the part over which we donĂÂżt have control. However it is shown that we can achieve any desirable inter-protocol fairness by properly choosing protocol parameters. Second, we propose a simple slow timescale source-based algorithm to decouple bandwidth allocation from router parameters and flow arrival patterns and prove its feasibility. The scheme needs only local information
Equilibrium of Heterogeneous Congestion Control: Optimality and Stability
When heterogeneous congestion control protocols
that react to different pricing signals share the same network,
the current theory based on utility maximization fails to predict
the network behavior. The pricing signals can be different types
of signals such as packet loss, queueing delay, etc, or different
values of the same type of signal such as different ECN marking
values based on the same actual link congestion level. Unlike in a
homogeneous network, the bandwidth allocation now depends on
router parameters and flow arrival patterns. It can be non-unique,
suboptimal and unstable. In Tang et al. (âEquilibrium of heterogeneous
congestion control: Existence and uniqueness,â IEEE/ACM
Trans. Netw., vol. 15, no. 4, pp. 824â837, Aug. 2007), existence and
uniqueness of equilibrium of heterogeneous protocols are investigated.
This paper extends the study with two objectives: analyzing
the optimality and stability of such networks and designing control
schemes to improve those properties. First, we demonstrate the
intricate behavior of a heterogeneous network through simulations
and present a framework to help understand its equilibrium
properties. Second, we propose a simple source-based algorithm
to decouple bandwidth allocation from router parameters and
flow arrival patterns by only updating a linear parameter in the
sourcesâ algorithms on a slow timescale. It steers a network to
the unique optimal equilibrium. The scheme can be deployed
incrementally as the existing protocol needs no change and only
new protocols need to adopt the slow timescale adaptation
A genetic algorithm for the design of a fuzzy controller for active queue management
Active queue management (AQM) policies are those
policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the
hosts on the network borders, and the adoption of a suitable control
policy. This paper proposes the adoption of a fuzzy proportional
integral (FPI) controller as an active queue manager for Internet
routers. The analytical design of the proposed FPI controller is
carried out in analogy with a proportional integral (PI) controller,
which recently has been proposed for AQM. A genetic algorithm is
proposed for tuning of the FPI controller parameters with respect
to optimal disturbance rejection. In the paper the FPI controller
design metodology is described and the results of the comparison
with random early detection (RED), tail drop, and PI controller
are presented
A quantitative analysis and performance study of fast congestion notification (FN) mechanism
Congestion in computer network happens when the number of transmission requests exceeds the transmission capacity at a certain network point (called a bottle-neck resource) at a specific time. Congestion usually causes buffers overflow and packets loss. The purpose of congestion management is to maintain a balance between the transmission requests and the transmission capacity so that the bottle-neck resources operate on an optimal level, and the sources are offered service in a way that assures fairness. Fast Congestion
Notification (FN) is one of the proactive queue management
mechanisms that limits the queuing delay and achieves the
maximum link utilization possible with minimum packet drops.
In this paper we present a detailed performance comparison of the Linear FN algorithm to RED based on the results obtained through simulations. The paper shows how FN can be tuned for different window size (Ws) and periods of time constant (T) to achieve higher link utilization; reduce the queuing delay, and lower packet drop ratio
Optimization flow control -- I: Basic algorithm and convergence
We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using a gradient projection algorithm. In this system, sources select transmission rates that maximize their own benefits, utility minus bandwidth cost, and network links adjust bandwidth prices to coordinate the sources' decisions. We allow feedback delays to be different, substantial, and time varying, and links and sources to update at different times and with different frequencies. We provide asynchronous distributed algorithms and prove their convergence in a static environment. We present measurements obtained from a preliminary prototype to illustrate the convergence of the algorithm in a slowly time-varying environment. We discuss its fairness property
TCP throughput guarantee in the DiffServ Assured Forwarding service: what about the results?
Since the proposition of Quality of Service architectures by the IETF, the
interaction between TCP and the QoS services has been intensively studied. This
paper proposes to look forward to the results obtained in terms of TCP
throughput guarantee in the DiffServ Assured Forwarding (DiffServ/AF) service
and to present an overview of the different proposals to solve the problem. It
has been demonstrated that the standardized IETF DiffServ conditioners such as
the token bucket color marker and the time sliding window color maker were not
good TCP traffic descriptors. Starting with this point, several propositions
have been made and most of them presents new marking schemes in order to
replace or improve the traditional token bucket color marker. The main problem
is that TCP congestion control is not designed to work with the AF service.
Indeed, both mechanisms are antagonists. TCP has the property to share in a
fair manner the bottleneck bandwidth between flows while DiffServ network
provides a level of service controllable and predictable. In this paper, we
build a classification of all the propositions made during these last years and
compare them. As a result, we will see that these conditioning schemes can be
separated in three sets of action level and that the conditioning at the
network edge level is the most accepted one. We conclude that the problem is
still unsolved and that TCP, conditioned or not conditioned, remains
inappropriate to the DiffServ/AF service
Implementation of Provably Stable MaxNet
MaxNet TCP is a congestion control protocol that uses explicit multi-bit signalling from routers to achieve desirable properties such as high throughput and low latency. In this paper we present an implementation of an extended version of MaxNet. Our contributions are threefold. First, we extend the original algorithm to give both provable stability and rate fairness. Second, we introduce the MaxStart algorithm which allows new MaxNet connections to reach their fair rates quickly. Third, we provide a Linux kernel implementation of the protocol. With no overhead but 24-bit price signals, our implementation scales from 32 bit/s to 1 peta-bit/s with a 0.001% rate accuracy. We confirm the theoretically predicted properties by performing a range of experiments at speeds up to 1 Gbit/sec and delays up to 180 ms on the WAN-in-Lab facility
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