1,250 research outputs found
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Dynamic Server Allocation over Time Varying Channels with Switchover Delay
We consider a dynamic server allocation problem over parallel queues with
randomly varying connectivity and server switchover delay between the queues.
At each time slot the server decides either to stay with the current queue or
switch to another queue based on the current connectivity and the queue length
information. Switchover delay occurs in many telecommunications applications
and is a new modeling component of this problem that has not been previously
addressed. We show that the simultaneous presence of randomly varying
connectivity and switchover delay changes the system stability region and the
structure of optimal policies. In the first part of the paper, we consider a
system of two parallel queues, and develop a novel approach to explicitly
characterize the stability region of the system using state-action frequencies
which are stationary solutions to a Markov Decision Process (MDP) formulation.
We then develop a frame-based dynamic control (FBDC) policy, based on the
state-action frequencies, and show that it is throughput-optimal asymptotically
in the frame length. The FBDC policy is applicable to a broad class of network
control systems and provides a new framework for developing throughput-optimal
network control policies using state-action frequencies. Furthermore, we
develop simple Myopic policies that provably achieve more than 90% of the
stability region. In the second part of the paper, we extend our results to
systems with an arbitrary but finite number of queues.Comment: 38 Pages, 18 figures. arXiv admin note: substantial text overlap with
arXiv:1008.234
Real-time characteristics of switched ethernet for "1553B" -embedded applications : simulation and analysis
In our previous work , Full Duplex Switched Ethernet was put forward as an attractive candidate to replace the MIL-STD 1553B data bus, in next generation "1553B"-embedded applications. An analytic study was conducted, using the Network Calculus formalism, to evaluate the deterministic guarantees offered by our proposal. Obtained results showed the effectiveness of traffic shaping techniques, combined with priority handling mechanisms on Full Duplex Switched Ethernet in order to satisfy 1553B-like real-time constraints. In this paper, we extend this work by the use of simulation. This gives the possibility to capture additional characteristics of the proposed architecture with respect to the analytical study, which was basically used to evaluate worst cases and deterministic guarantees. Hence, to assess the real-time characteristics of our proposed interconnection technology, the results yielded by simulation are discussed and average latencies distributions are considered
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. (“Equilibrium of heterogeneous
congestion control: Existence and uniqueness,” IEEE/ACM
Trans. Netw., vol. 15, no. 4, pp. 824–837, 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
sources’ 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
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
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