2,554 research outputs found
Optimizing Age-of-Information in a Multi-class Queueing System
We consider the age-of-information in a multi-class 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
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
Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals
We consider a wireless network with a base station serving multiple traffic
streams to different destinations. Packets from each stream arrive to the base
station according to a stochastic process and are enqueued in a separate (per
stream) queue. The queueing discipline controls which packet within each queue
is available for transmission. The base station decides, at every time t, which
stream to serve to the corresponding destination. The goal of scheduling
decisions is to keep the information at the destinations fresh. Information
freshness is captured by the Age of Information (AoI) metric.
In this paper, we derive a lower bound on the AoI performance achievable by
any given network operating under any queueing discipline. Then, we consider
three common queueing disciplines and develop both an Optimal Stationary
Randomized policy and a Max-Weight policy under each discipline. Our approach
allows us to evaluate the combined impact of the stochastic arrivals, queueing
discipline and scheduling policy on AoI. We evaluate the AoI performance both
analytically and using simulations. Numerical results show that the performance
of the Max-Weight policy is close to the analytical lower bound
Age-Optimal Updates of Multiple Information Flows
In this paper, we study an age of information minimization problem, where
multiple flows of update packets are sent over multiple servers to their
destinations. Two online scheduling policies are proposed. When the packet
generation and arrival times are synchronized across the flows, the proposed
policies are shown to be (near) optimal for minimizing any time-dependent,
symmetric, and non-decreasing penalty function of the ages of the flows over
time in a stochastic ordering sense
Update or Wait: How to Keep Your Data Fresh
In this work, we study how to optimally manage the freshness of information
updates sent from a source node to a destination via a channel. A proper metric
for data freshness at the destination is the age-of-information, or simply age,
which is defined as how old the freshest received update is since the moment
that this update was generated at the source node (e.g., a sensor). A
reasonable update policy is the zero-wait policy, i.e., the source node submits
a fresh update once the previous update is delivered and the channel becomes
free, which achieves the maximum throughput and the minimum delay.
Surprisingly, this zero-wait policy does not always minimize the age. This
counter-intuitive phenomenon motivates us to study how to optimally control
information updates to keep the data fresh and to understand when the zero-wait
policy is optimal. We introduce a general age penalty function to characterize
the level of dissatisfaction on data staleness and formulate the average age
penalty minimization problem as a constrained semi-Markov decision problem
(SMDP) with an uncountable state space. We develop efficient algorithms to find
the optimal update policy among all causal policies, and establish sufficient
and necessary conditions for the optimality of the zero-wait policy. Our
investigation shows that the zero-wait policy is far from the optimum if (i)
the age penalty function grows quickly with respect to the age, (ii) the packet
transmission times over the channel are positively correlated over time, or
(iii) the packet transmission times are highly random (e.g., following a
heavy-tail distribution)
Uplink Age of Information of Unilaterally Powered Two-way Data Exchanging Systems
We consider a two-way data exchanging system where a master node transfers
energy and data packets to a slave node alternatively. The slave node harvests
the transferred energy and performs information transmission as long as it has
sufficient energy for current block, i.e., according to the best-effort policy.
We examine the freshness of the received packets at the master node in terms of
age of information (AoI), which is defined as the time elapsed after the
generation of the latest received packet. We derive average uplink AoI and
uplink data rate as functions of downlink data rate in closed form. The
obtained results illustrate the performance limit of the unilaterally powered
two-way data exchanging system in terms of timeliness and efficiency. The
results also specify the achievable tradeoff between the data rates of the
two-way data exchanging system.Comment: INFOCOM 2018 AOI Wkshp, 6 page
Optimizing Age of Information in Wireless Networks with Perfect Channel State Information
Age of information (AoI), defined as the time elapsed since the last received
update was generated, is a newly proposed metric to measure the timeliness of
information updates in a network. We consider AoI minimization problem for a
network with general interference constraints, and time varying channels. We
propose two policies, namely, virtual-queue based policy and age-based policy
when the channel state is available to the network scheduler at each time step.
We prove that the virtual-queue based policy is nearly optimal, up to a
constant additive factor, and the age-based policy is at-most factor 4 away
from optimality. Comparing with our previous work, which derived age optimal
policies when channel state information is not available to the scheduler, we
demonstrate a 4 fold improvement in age due to the availability of channel
state information
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