2,120 research outputs found
Content Distribution by Multiple Multicast Trees and Intersession Cooperation: Optimal Algorithms and Approximations
In traditional massive content distribution with multiple sessions, the
sessions form separate overlay networks and operate independently, where some
sessions may suffer from insufficient resources even though other sessions have
excessive resources. To cope with this problem, we consider the universal
swarming approach, which allows multiple sessions to cooperate with each other.
We formulate the problem of finding the optimal resource allocation to maximize
the sum of the session utilities and present a subgradient algorithm which
converges to the optimal solution in the time-average sense. The solution
involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope
with this difficulty by using a column generation method, which reduces the
number of Steiner-tree computations. Furthermore, we allow the use of
approximate solutions to the Steiner-tree subproblem. We show that the
approximation ratio to the overall problem turns out to be no less than the
reciprocal of the approximation ratio to the Steiner-tree subproblem.
Simulation results demonstrate that universal swarming improves the performance
of resource-poor sessions with negligible impact to resource-rich sessions. The
proposed approach and algorithm are expected to be useful for
infrastructure-based content distribution networks with long-lasting sessions
and relatively stable network environment
On Resource Allocation in Fading Multiple Access Channels - An Efficient Approximate Projection Approach
We consider the problem of rate and power allocation in a multiple-access
channel. Our objective is to obtain rate and power allocation policies that
maximize a general concave utility function of average transmission rates on
the information theoretic capacity region of the multiple-access channel. Our
policies does not require queue-length information. We consider several
different scenarios. First, we address the utility maximization problem in a
nonfading channel to obtain the optimal operating rates, and present an
iterative gradient projection algorithm that uses approximate projection. By
exploiting the polymatroid structure of the capacity region, we show that the
approximate projection can be implemented in time polynomial in the number of
users. Second, we consider resource allocation in a fading channel. Optimal
rate and power allocation policies are presented for the case that power
control is possible and channel statistics are available. For the case that
transmission power is fixed and channel statistics are unknown, we propose a
greedy rate allocation policy and provide bounds on the performance difference
of this policy and the optimal policy in terms of channel variations and
structure of the utility function. We present numerical results that
demonstrate superior convergence rate performance for the greedy policy
compared to queue-length based policies. In order to reduce the computational
complexity of the greedy policy, we present approximate rate allocation
policies which track the greedy policy within a certain neighborhood that is
characterized in terms of the speed of fading.Comment: 32 pages, Submitted to IEEE Trans. on Information Theor
Fluid flow queue models for fixed-mobile network evaluation
A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios
Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks
This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc
wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints.
By dual decomposition, the resource allocation problem
naturally decomposes into three subproblems: congestion control,
routing and scheduling that interact through congestion price.
The global convergence property of this algorithm is proved. We
next extend the dual algorithm to handle networks with timevarying
channels and adaptive multi-rate devices. The stability
of the resulting system is established, and its performance is
characterized with respect to an ideal reference system which
has the best feasible rate region at link layer.
We then generalize the aforementioned results to a general
model of queueing network served by a set of interdependent
parallel servers with time-varying service capabilities, which
models many design problems in communication networks. We
show that for a general convex optimization problem where a
subset of variables lie in a polytope and the rest in a convex set,
the dual-based algorithm remains stable and optimal when the
constraint set is modulated by an irreducible finite-state Markov
chain. This paper thus presents a step toward a systematic way
to carry out cross-layer design in the framework of “layering as
optimization decomposition” for time-varying channel models
NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming
In recent years, there has been an increasing demand for ubiquitous streaming
like applications in data networks. In this paper, we concentrate on NUM-based
rate allocation for streaming applications with the so-called S-curve utility
functions. Due to non-concavity of such utility functions, the underlying NUM
problem would be non-convex for which dual methods might become quite useless.
To tackle the non-convex problem, using elementary techniques we make the
utility of the network concave, however this results in reverse-convex
constraints which make the problem non-convex. To deal with such a transformed
NUM, we leverage Sequential Convex Programming (SCP) approach to approximate
the non-convex problem by a series of convex ones. Based on this approach, we
propose a distributed rate allocation algorithm and demonstrate that under mild
conditions, it converges to a locally optimal solution of the original NUM.
Numerical results validate the effectiveness, in terms of tractable convergence
of the proposed rate allocation algorithm.Comment: 6 pages, conference submissio
Accelerated Backpressure Algorithm
We develop an Accelerated Back Pressure (ABP) algorithm using Accelerated
Dual Descent (ADD), a distributed approximate Newton-like algorithm that only
uses local information. Our construction is based on writing the backpressure
algorithm as the solution to a network feasibility problem solved via
stochastic dual subgradient descent. We apply stochastic ADD in place of the
stochastic gradient descent algorithm. We prove that the ABP algorithm
guarantees stable queues. Our numerical experiments demonstrate a significant
improvement in convergence rate, especially when the packet arrival statistics
vary over time.Comment: 9 pages, 4 figures. A version of this work with significantly
extended proofs is being submitted for journal publicatio
Distributed Random Access Algorithm: Scheduling and Congesion Control
This paper provides proofs of the rate stability, Harris recurrence, and
epsilon-optimality of CSMA algorithms where the backoff parameter of each node
is based on its backlog. These algorithms require only local information and
are easy to implement.
The setup is a network of wireless nodes with a fixed conflict graph that
identifies pairs of nodes whose simultaneous transmissions conflict. The paper
studies two algorithms. The first algorithm schedules transmissions to keep up
with given arrival rates of packets. The second algorithm controls the arrivals
in addition to the scheduling and attempts to maximize the sum of the utilities
of the flows of packets at the different nodes. For the first algorithm, the
paper proves rate stability for strictly feasible arrival rates and also Harris
recurrence of the queues. For the second algorithm, the paper proves the
epsilon-optimality. Both algorithms operate with strictly local information in
the case of decreasing step sizes, and operate with the additional information
of the number of nodes in the network in the case of constant step size
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