24 research outputs found
The Power of Online Learning in Stochastic Network Optimization
In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, and , that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: and achieve the
near-optimal utility-delay tradeoff
and possesses an convergence time.
and are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice
The Power of Online Learning in Stochastic Network Optimization
In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, and , that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: and achieve the
near-optimal utility-delay tradeoff
and possesses an convergence time.
and are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice
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
Integrating wireless technologies into intra-vehicular communication
With the emergence of connected and autonomous vehicles, sensors are increasingly deployed within car. Traffic generated by these sensors congest traditional intra-vehicular networks, such as CAN buses. Furthermore, the large amount of wires needed to connect sensors makes it hard to design cars in a modular way. These limitations have created impetus to use wireless technologies to support intra-vehicular communication. In this dissertation, we tackle the challenge of designing and evaluating data collection protocols for intra-car networks that can operate reliably and efficiently under dynamic channel conditions.
First, we evaluate the feasibility of deploying an intra-car wireless network based on the Backpressure Collection Protocol (BCP), which is theoretically proven to be throughput-optimal.
We uncover a surprising behavior in which, under certain dynamic channel conditions, the average packet delay of BCP decreases with the traffic load. We propose and analyze a queueing-theoretic model to shed light into the observed phenomenon. As a solution, we propose a new protocol, called replication-based LIFO-backpressure (RBL). Analytical and simulation results indicate that RBL dramatically reduces the delay of BCP at low load, while maintaining its high throughput performance.
Next, we propose and implement a hybrid wired/wireless architecture, in which each node is connected to either a wired interface or a wireless interface or both. We propose a new protocol, called Hybrid-Backpressure Collection Protocol (Hybrid-BCP), for the intra-car hybrid networks. Our testbed implementation, based on CAN and ZigBee transceivers, demonstrates the load balancing and routing functionalities of Hybrid-BCP and its resilience to DoS attacks. We further provide simulation results, obtained based on real intra-car RSSI traces, showing that Hybrid-BCP can achieve the same performance as a tree-based protocol while reducing the radio transmission power by a factor of 10.
Finally, we present TeaCP, a prototype Toolkit for the evaluation and analysis of Collection Protocols in both simulation and experimental environments. TeaCP evaluates a wide range of standard performance metrics, such as reliability, throughput, and latency. TeaCP further allows visualization of routes and network topology evolution. Through simulation of an intra-car WSN and real lab experiments, we demonstrate the functionality of TeaCP for comparing different collection protocols
Fast-Convergent Learning-aided Control in Energy Harvesting Networks
In this paper, we present a novel learning-aided energy management scheme
() for multihop energy harvesting networks. Different from prior
works on this problem, our algorithm explicitly incorporates information
learning into system control via a step called \emph{perturbed dual learning}.
does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that achieves the near-optimal
utility-delay tradeoff with an
energy buffers (). More interestingly,
possesses a \emph{convergence time} of , which is much faster than the time of
pure queue-based techniques or the time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of
demonstrate how system control algorithms can be augmented by learning and what
the benefits are. The methodology and algorithm can also be applied to similar
problems, e.g., processing networks, where nodes require nonzero amount of
contents to support their actions
Learning-aided Stochastic Network Optimization with Imperfect State Prediction
We investigate the problem of stochastic network optimization in the presence
of imperfect state prediction and non-stationarity. Based on a novel
distribution-accuracy curve prediction model, we develop the predictive
learning-aided control (PLC) algorithm, which jointly utilizes historic and
predicted network state information for decision making. PLC is an online
algorithm that requires zero a-prior system statistical information, and
consists of three key components, namely sequential distribution estimation and
change detection, dual learning, and online queue-based control.
Specifically, we show that PLC simultaneously achieves good long-term
performance, short-term queue size reduction, accurate change detection, and
fast algorithm convergence. In particular, for stationary networks, PLC
achieves a near-optimal , utility-delay
tradeoff. For non-stationary networks, \plc{} obtains an
utility-backlog tradeoff for distributions that last
time, where
is the prediction accuracy and is a constant (the
Backpressue algorithm \cite{neelynowbook} requires an length
for the same utility performance with a larger backlog). Moreover, PLC detects
distribution change slots faster with high probability ( is the
prediction size) and achieves an convergence time. Our results demonstrate
that state prediction (even imperfect) can help (i) achieve faster detection
and convergence, and (ii) obtain better utility-delay tradeoffs