8,368 research outputs found
Fixed-Point Approximations of Bandwidth-Sharing Networks with Rate Constraints
Bandwidth-sharing networks are important flow level models of communication networks. We focus on the fact that it takes a signicant number of users to saturate a link, necessitating the inclusion of individual rate constraints. In particular we extend work of Reed & Zwart on fluid models of bandwidth sharing with rate constraints under Markovian assumptions: we consider a bandwidth sharing network with rate constraints, where job sizes and deadlines have a general joint distribution. We introduce a
fluid model and investigate several of its properties. In particular we show that its invariant point approximates the invariant distribution of the bandwidth sharing network if capacities are large
Fluid model for a network operating under a fair bandwidth-sharing policy
We consider a model of Internet congestion control that represents the
randomly varying number of flows present in a network where bandwidth is shared
fairly between document transfers. We study critical fluid models obtained as
formal limits under law of large numbers scalings when the average load on at
least one resource is equal to its capacity. We establish convergence to
equilibria for fluid models and identify the invariant manifold.
The form of the invariant manifold gives insight into the phenomenon of
entrainment whereby congestion at some resources may prevent other resources
from working at their full capacity
Multiplexing regulated traffic streams: design and performance
The main network solutions for supporting QoS rely on traf- fic policing (conditioning, shaping). In particular, for IP networks the IETF has developed Intserv (individual flows regulated) and Diffserv (only ag- gregates regulated). The regulator proposed could be based on the (dual) leaky-bucket mechanism. This explains the interest in network element per- formance (loss, delay) for leaky-bucket regulated traffic. This paper describes a novel approach to the above problem. Explicitly using the correlation structure of the sourcesā traffic, we derive approxi- mations for both small and large buffers. Importantly, for small (large) buffers the short-term (long-term) correlations are dominant. The large buffer result decomposes the traffic stream in a stream of constant rate and a periodic impulse stream, allowing direct application of the Brownian bridge approximation. Combining the small and large buffer results by a concave majorization, we propose a simple, fast and accurate technique to statistically multiplex homogeneous regulated sources. To address heterogeneous inputs, we present similarly efficient tech- niques to evaluate the performance of multiple classes of traffic, each with distinct characteristics and QoS requirements. These techniques, applica- ble under more general conditions, are based on optimal resource (band- width and buffer) partitioning. They can also be directly applied to set GPS (Generalized Processor Sharing) weights and buffer thresholds in a shared resource system
A Stochastic Resource-Sharing Network for Electric Vehicle Charging
We consider a distribution grid used to charge electric vehicles such that
voltage drops stay bounded. We model this as a class of resource-sharing
networks, known as bandwidth-sharing networks in the communication network
literature. We focus on resource-sharing networks that are driven by a class of
greedy control rules that can be implemented in a decentralized fashion. For a
large number of such control rules, we can characterize the performance of the
system by a fluid approximation. This leads to a set of dynamic equations that
take into account the stochastic behavior of EVs. We show that the invariant
point of these equations is unique and can be computed by solving a specific
ACOPF problem, which admits an exact convex relaxation. We illustrate our
findings with a case study using the SCE 47-bus network and several special
cases that allow for explicit computations.Comment: 13 pages, 8 figure
State space collapse and diffusion approximation for a network operating under a fair bandwidth sharing policy
We consider a connection-level model of Internet congestion control,
introduced by Massouli\'{e} and Roberts [Telecommunication Systems 15 (2000)
185--201], that represents the randomly varying number of flows present in a
network. Here, bandwidth is shared fairly among elastic document transfers
according to a weighted -fair bandwidth sharing policy introduced by Mo
and Walrand [IEEE/ACM Transactions on Networking 8 (2000) 556--567] []. Assuming Poisson arrivals and exponentially distributed document
sizes, we focus on the heavy traffic regime in which the average load placed on
each resource is approximately equal to its capacity. A fluid model (or
functional law of large numbers approximation) for this stochastic model was
derived and analyzed in a prior work [Ann. Appl. Probab. 14 (2004) 1055--1083]
by two of the authors. Here, we use the long-time behavior of the solutions of
the fluid model established in that paper to derive a property called
multiplicative state space collapse, which, loosely speaking, shows that in
diffusion scale, the flow count process for the stochastic model can be
approximately recovered as a continuous lifting of the workload process.Comment: Published in at http://dx.doi.org/10.1214/08-AAP591 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Proportional fairness and its relationship with multi-class queueing networks
We consider multi-class single-server queueing networks that have a product
form stationary distribution. A new limit result proves a sequence of such
networks converges weakly to a stochastic flow level model. The stochastic flow
level model found is insensitive. A large deviation principle for the
stationary distribution of these multi-class queueing networks is also found.
Its rate function has a dual form that coincides with proportional fairness. We
then give the first rigorous proof that the stationary throughput of a
multi-class single-server queueing network converges to a proportionally fair
allocation. This work combines classical queueing networks with more recent
work on stochastic flow level models and proportional fairness. One could view
these seemingly different models as the same system described at different
levels of granularity: a microscopic, queueing level description; a
macroscopic, flow level description and a teleological, optimization
description.Comment: Published in at http://dx.doi.org/10.1214/09-AAP612 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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