16,197 research outputs found
On Stability Region and Delay Performance of Linear-Memory Randomized Scheduling for Time-Varying Networks
Throughput optimal scheduling policies in general require the solution of a
complex and often NP-hard optimization problem. Related literature has shown
that in the context of time-varying channels, randomized scheduling policies
can be employed to reduce the complexity of the optimization problem but at the
expense of a memory requirement that is exponential in the number of data
flows. In this paper, we consider a Linear-Memory Randomized Scheduling Policy
(LM-RSP) that is based on a pick-and-compare principle in a time-varying
network with one-hop data flows. For general ergodic channel processes, we
study the performance of LM-RSP in terms of its stability region and average
delay. Specifically, we show that LM-RSP can stabilize a fraction of the
capacity region. Our analysis characterizes this fraction as well as the
average delay as a function of channel variations and the efficiency of LM-RSP
in choosing an appropriate schedule vector. Applying these results to a class
of Markovian channels, we provide explicit results on the stability region and
delay performance of LM-RSP.Comment: Long version of preprint to appear in the IEEE Transactions on
Networkin
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
Robust And Optimal Opportunistic Scheduling For Downlink 2-Flow Network Coding With Varying Channel Quality and Rate Adaptation
This paper considers the downlink traffic from a base station to two
different clients. When assuming infinite backlog, it is known that
inter-session network coding (INC) can significantly increase the throughput of
each flow. However, the corresponding scheduling solution (when assuming
dynamic arrivals instead and requiring bounded delay) is still nascent.
For the 2-flow downlink scenario, we propose the first opportunistic INC +
scheduling solution that is provably optimal for time-varying channels, i.e.,
the corresponding stability region matches the optimal Shannon capacity.
Specifically, we first introduce a new binary INC operation, which is
distinctly different from the traditional wisdom of XORing two overheard
packets. We then develop a queue-length-based scheduling scheme, which, with
the help of the new INC operation, can robustly and optimally adapt to
time-varying channel quality. We then show that the proposed algorithm can be
easily extended for rate adaptation and it again robustly achieves the optimal
throughput. A byproduct of our results is a scheduling scheme for stochastic
processing networks (SPNs) with random departure, which relaxes the assumption
of deterministic departure in the existing results. The new SPN scheduler could
thus further broaden the applications of SPN scheduling to other real-world
scenarios
Dynamic Control of Tunable Sub-optimal Algorithms for Scheduling of Time-varying Wireless Networks
It is well known that for ergodic channel processes the Generalized
Max-Weight Matching (GMWM) scheduling policy stabilizes the network for any
supportable arrival rate vector within the network capacity region. This
policy, however, often requires the solution of an NP-hard optimization
problem. This has motivated many researchers to develop sub-optimal algorithms
that approximate the GMWM policy in selecting schedule vectors. One implicit
assumption commonly shared in this context is that during the algorithm
runtime, the channel states remain effectively unchanged. This assumption may
not hold as the time needed to select near-optimal schedule vectors usually
increases quickly with the network size. In this paper, we incorporate channel
variations and the time-efficiency of sub-optimal algorithms into the scheduler
design, to dynamically tune the algorithm runtime considering the tradeoff
between algorithm efficiency and its robustness to changing channel states.
Specifically, we propose a Dynamic Control Policy (DCP) that operates on top of
a given sub-optimal algorithm, and dynamically but in a large time-scale
adjusts the time given to the algorithm according to queue backlog and channel
correlations. This policy does not require knowledge of the structure of the
given sub-optimal algorithm, and with low overhead can be implemented in a
distributed manner. Using a novel Lyapunov analysis, we characterize the
throughput stability region induced by DCP and show that our characterization
can be tight. We also show that the throughput stability region of DCP is at
least as large as that of any other static policy. Finally, we provide two case
studies to gain further intuition into the performance of DCP.Comment: Submitted for journal consideration. A shorter version was presented
in IEEE IWQoS 200
Throughput Optimal Scheduling with Dynamic Channel Feedback
It is well known that opportunistic scheduling algorithms are throughput
optimal under full knowledge of channel and network conditions. However, these
algorithms achieve a hypothetical achievable rate region which does not take
into account the overhead associated with channel probing and feedback required
to obtain the full channel state information at every slot. We adopt a channel
probing model where fraction of time slot is consumed for acquiring the
channel state information (CSI) of a single channel. In this work, we design a
joint scheduling and channel probing algorithm named SDF by considering the
overhead of obtaining the channel state information. We first analytically
prove SDF algorithm can support fraction of of the full rate
region achieved when all users are probed where depends on the
expected number of users which are not probed. Then, for homogenous channel, we
show that when the number of users in the network is greater than 3, , i.e., we guarantee to expand the rate region. In addition, for
heterogenous channels, we prove the conditions under which SDF guarantees to
increase the rate region. We also demonstrate numerically in a realistic
simulation setting that this rate region can be achieved by probing only less
than 50% of all channels in a CDMA based cellular network utilizing high data
rate protocol under normal channel conditions.Comment: submitte
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