919 research outputs found
When Backpressure Meets Predictive Scheduling
Motivated by the increasing popularity of learning and predicting human user
behavior in communication and computing systems, in this paper, we investigate
the fundamental benefit of predictive scheduling, i.e., predicting and
pre-serving arrivals, in controlled queueing systems. Based on a lookahead
window prediction model, we first establish a novel equivalence between the
predictive queueing system with a \emph{fully-efficient} scheduling scheme and
an equivalent queueing system without prediction. This connection allows us to
analytically demonstrate that predictive scheduling necessarily improves system
delay performance and can drive it to zero with increasing prediction power. We
then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving
optimal utility performance in such predictive systems. \textsf{PBP}
efficiently incorporates prediction into stochastic system control and avoids
the great complication due to the exponential state space growth in the
prediction window size. We show that \textsf{PBP} can achieve a utility
performance that is within of the optimal, for any ,
while guaranteeing that the system delay distribution is a
\emph{shifted-to-the-left} version of that under the original Backpressure
algorithm. Hence, the average packet delay under \textsf{PBP} is strictly
better than that under Backpressure, and vanishes with increasing prediction
window size. This implies that the resulting utility-delay tradeoff with
predictive scheduling beats the known optimal tradeoff for systems without prediction
TCP-Aware Backpressure Routing and Scheduling
In this work, we explore the performance of backpressure routing and
scheduling for TCP flows over wireless networks. TCP and backpressure are not
compatible due to a mismatch between the congestion control mechanism of TCP
and the queue size based routing and scheduling of the backpressure framework.
We propose a TCP-aware backpressure routing and scheduling that takes into
account the behavior of TCP flows. TCP-aware backpressure (i) provides
throughput optimality guarantees in the Lyapunov optimization framework, (ii)
gracefully combines TCP and backpressure without making any changes to the TCP
protocol, (iii) improves the throughput of TCP flows significantly, and (iv)
provides fairness across competing TCP flows
Store-Forward and its implications for Proportional Scheduling
The Proportional Scheduler was recently proposed as a scheduling algorithm
for multi-hop switch networks. For these networks, the BackPressure scheduler
is the classical benchmark. For networks with fixed routing, the Proportional
Scheduler is maximum stable, myopic and, furthermore, will alleviate certain
scaling issued found in BackPressure for large networks. Nonetheless, the
equilibrium and delay properties of the Proportional Scheduler has not been
fully characterized.
In this article, we postulate on the equilibrium behaviour of the
Proportional Scheduler though the analysis of an analogous rule called the
Store-Forward allocation. It has been shown that Store-Forward has
asymptotically allocates according to the Proportional Scheduler. Further, for
Store-Forward networks, numerous equilibrium quantities are explicitly
calculable. For FIFO networks under Store-Forward, we calculate the policies
stationary distribution and end-to-end route delay. We discuss network
topologies when the stationary distribution is product-form, a phenomenon which
we call \emph{product form resource pooling}. We extend this product form
notion to independent set scheduling on perfect graphs, where we show that
non-neighbouring queues are statistically independent. Finally, we analyse the
large deviations behaviour of the equilibrium distribution of Store-Forward
networks in order to construct Lyapunov functions for FIFO switch networks
Device-Aware Routing and Scheduling in Multi-Hop Device-to-Device Networks
The dramatic increase in data and connectivity demand, in addition to
heterogeneous device capabilities, poses a challenge for future wireless
networks. One of the promising solutions is Device-to-Device (D2D) networking.
D2D networking, advocating the idea of connecting two or more devices directly
without traversing the core network, is promising to address the increasing
data and connectivity demand. In this paper, we consider D2D networks, where
devices with heterogeneous capabilities including computing power, energy
limitations, and incentives participate in D2D activities heterogeneously. We
develop (i) a device-aware routing and scheduling algorithm (DARS) by taking
into account device capabilities, and (ii) a multi-hop D2D testbed using
Android-based smartphones and tablets by exploiting Wi-Fi Direct and legacy
Wi-Fi connections. We show that DARS significantly improves throughput in our
testbed as compared to state-of-the-art
Device-Centric Cooperation in Mobile Networks
The increasing popularity of applications such as video streaming in today's
mobile devices introduces higher demand for throughput, and puts a strain
especially on cellular links. Cooperation among mobile devices by exploiting
both cellular and local area connections is a promising approach to meet the
increasing demand. In this paper, we consider that a group of cooperative
mobile devices, exploiting both cellular and local area links and within
proximity of each other, are interested in the same video content. Traditional
network control algorithms introduce high overhead and delay in this setup as
the network control and cooperation decisions are made in a source-centric
manner. Instead, we develop a device-centric stochastic cooperation scheme. Our
device-centric scheme; DcC allows mobile devices to make control decisions such
as flow control, scheduling, and cooperation without loss of optimality. Thanks
to being device-centric, DcC reduces; (i) overhead; i.e., the number of control
packets that should be transmitted over cellular links, so cellular links are
used more efficiently, and (ii) the amount of delay that each packet
experiences, which improves quality of service. The simulation results
demonstrate the benefits of DcC
Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization
Distributed network optimization has been studied for well over a decade.
However, we still do not have a good idea of how to design schemes that can
simultaneously provide good performance across the dimensions of utility
optimality, convergence speed, and delay. To address these challenges, in this
paper, we propose a new algorithmic framework with all these metrics
approaching optimality. The salient features of our new algorithm are
three-fold: (i) fast convergence: it converges with only
iterations that is the fastest speed among all the existing algorithms; (ii)
low delay: it guarantees optimal utility with finite queue length; (iii) simple
implementation: the control variables of this algorithm are based on virtual
queues that do not require maintaining per-flow information. The new technique
builds on a kind of inexact Uzawa method in the Alternating Directional Method
of Multiplier, and provides a new theoretical path to prove global and linear
convergence rate of such a method without requiring the full rank assumption of
the constraint matrix
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