95 research outputs found

### Fast-Convergent Learning-aided Control in Energy Harvesting Networks

In this paper, we present a novel learning-aided energy management scheme
($\mathtt{LEM}$) 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}.
$\mathtt{LEM}$ does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that $\mathtt{LEM}$ achieves the near-optimal
$[O(\epsilon), O(\log(1/\epsilon)^2)]$ utility-delay tradeoff with an
$O(1/\epsilon^{1-c/2})$ energy buffers ($c\in(0,1)$). More interestingly,
$\mathtt{LEM}$ possesses a \emph{convergence time} of $O(1/\epsilon^{1-c/2}
+1/\epsilon^c)$, which is much faster than the $\Theta(1/\epsilon)$ time of
pure queue-based techniques or the $\Theta(1/\epsilon^2)$ time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes $\mathtt{LEM}$ more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of $\mathtt{LEM}$
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

### Optimizing Your Online-Advertisement Asynchronously

We consider the problem of designing optimal online-ad investment strategies
for a single advertiser, who invests at multiple sponsored search sites
simultaneously, with the objective of maximizing his average revenue subject to
the advertising budget constraint. A greedy online investment scheme is
developed to achieve an average revenue that can be pushed to within
$O(\epsilon)$ of the optimal, for any $\epsilon>0$, with a tradeoff that the
temporal budget violation is $O(1/\epsilon)$. Different from many existing
algorithms, our scheme allows the advertiser to \emph{asynchronously} update
his investments on each search engine site, hence applies to systems where the
timescales of action update intervals are heterogeneous for different sites. We
also quantify the impact of inaccurate estimation of the system dynamics and
show that the algorithm is robust against imperfect system knowledge

### Timely-Throughput Optimal Scheduling with Prediction

Motivated by the increasing importance of providing delay-guaranteed services
in general computing and communication systems, and the recent wide adoption of
learning and prediction in network control, in this work, we consider a general
stochastic single-server multi-user system and investigate the fundamental
benefit of predictive scheduling in improving timely-throughput, being the rate
of packets that are delivered to destinations before their deadlines. By
adopting an error rate-based prediction model, we first derive a Markov
decision process (MDP) solution to optimize the timely-throughput objective
subject to an average resource consumption constraint. Based on a packet-level
decomposition of the MDP, we explicitly characterize the optimal scheduling
policy and rigorously quantify the timely-throughput improvement due to
predictive-service, which scales as
$\Theta(p\left[C_{1}\frac{(a-a_{\max}q)}{p-q}\rho^{\tau}+C_{2}(1-\frac{1}{p})\right](1-\rho^{D}))$,
where $a, a_{\max}, \rho\in(0, 1), C_1>0, C_2\ge0$ are constants, $p$ is the
true-positive rate in prediction, $q$ is the false-negative rate, $\tau$ is the
packet deadline and $D$ is the prediction window size. We also conduct
extensive simulations to validate our theoretical findings. Our results provide
novel insights into how prediction and system parameters impact performance and
provide useful guidelines for designing predictive low-latency control
algorithms.Comment: 14 pages, 7 figure

### Optimizing Age-of-Information in a Multi-class Queueing System

We consider the age-of-information in a multi-class $M/G/1$ queueing system,
where each class generates packets containing status information. Age of
information is a relatively new metric that measures the amount of time that
elapsed between status updates, thus accounting for both the queueing delay and
the delay between packet generation. This gives rise to a tradeoff between
frequency of status updates, and queueing delay. In this paper, we study this
tradeoff in a system with heterogenous users modeled as a multi-class $M/G/1$
queue. To this end, we derive the exact peak age-of-Information (PAoI) profile
of the system, which measures the "freshness" of the status information. We
then seek to optimize the age of information, by formulating the problem using
quasiconvex optimization, and obtain structural properties of the optimal
solution

### Dynamic Product Assembly and Inventory Control for Maximum Profit

We consider a manufacturing plant that purchases raw materials for product
assembly and then sells the final products to customers. There are M types of
raw materials and K types of products, and each product uses a certain subset
of raw materials for assembly. The plant operates in slotted time, and every
slot it makes decisions about re-stocking materials and pricing the existing
products in reaction to (possibly time-varying) material costs and consumer
demands. We develop a dynamic purchasing and pricing policy that yields time
average profit within epsilon of optimality, for any given epsilon>0, with a
worst case storage buffer requirement that is O(1/epsilon). The policy can be
implemented easily for large M, K, yields fast convergence times, and is robust
to non-ergodic system dynamics.Comment: 32 page

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