18 research outputs found
Benefits of Coding on Age of Information in Broadcast Networks
Age of Information (AoI) is studied in two-user broadcast networks with
feedback, and lower and upper bounds are derived on the expected weighted sum
AoI of the users. In particular, a class of simple coding actions is considered
and within this class, randomized and deterministic policies are devised.
Explicit conditions are found for symmetric dependent channels under which
coded randomized policies strictly outperform the corresponding uncoded
policies. Similar behavior is numerically shown for deterministic policies
Age of Information Aware Cache Updating with File- and Age-Dependent Update Durations
We consider a system consisting of a library of time-varying files, a server
that at all times observes the current version of all files, and a cache that
at the beginning stores the current versions of all files but afterwards has to
update %fresh versions of these files from the server. Unlike previous works,
the update duration is not constant but depends on the file and its Age of
Information (AoI), i.e., of the time elapsed since it was last updated. The
goal of this work is to design an update policy that minimizes the average AoI
of all files with respect to a given popularity distribution. Actually a
relaxed problem, close to the original optimization problem, is solved and a
practical update policy is derived. The update policy relies on the file
popularity and on the functions that characterize the update durations of the
files depending on their AoI. Numerical simulations show a significant
improvement of this new update policy compared to the so-called square-root
policy that is optimal under file-independent and constant update durations.Comment: To be submitted to ICC 202
Maximizing Information Freshness in Caching Systems with Limited Cache Storage Capacity
We consider a cache updating system with a source, a cache with limited
storage capacity and a user. There are files. The source keeps the freshest
versions of the files which are updated with known rates. The cache gets fresh
files from the source, but it can only store the latest downloaded versions of
files where . The user gets the files either from the cache or
from the source. If the user gets the files from the cache, the received files
might be outdated depending on the file status at the source. If the user gets
the files directly from the source, then the received files are always fresh,
but the extra transmission times between the source and the user decreases the
freshness at the user. Thus, we study the trade-off between storing the files
at the cache and directly obtaining the files from the source at the expense of
additional transmission times. We find analytical expressions for the average
freshness of the files at the user for both of these scenarios. Then, we find
the optimal caching status for each file (i.e., whether to store the file at
the cache or not) and the corresponding file update rates at the cache to
maximize the overall freshness at the user. We observe that when the total
update rate of the cache is high, caching files improves the freshness at the
user. However, when the total update rate of the cache is low, the optimal
policy for the user is to obtain the frequently changing files and the files
that have relatively small transmission times directly from the source.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0947
Accounting for Information Freshness in Scheduling of Content Caching
In this paper, we study the problem of optimal scheduling of content
placement along time in a base station with limited cache capacity, taking into
account jointly the offloading effect and freshness of information. We model
offloading based on popularity in terms of the number of requests and
information freshness based on the notion of age of information (AoI). The
objective is to reduce the load of backhaul links as well as the AoI of
contents in the cache via a joint cost function. For the resulting optimization
problem, we prove its hardness via a reduction from the Partition problem.
Next, via a mathematical reformulation, we derive a solution approach based on
column generation and a tailored rounding mechanism. Finally, we provide
performance evaluation results showing that our algorithm provides near-optimal
solutions
Age of Information with Gilbert-Elliot Servers and Samplers
We study age of information in a status updating system that consists of a
single sampler, i.e., source node, that sends time-sensitive status updates to
a single monitor node through a server node. We first consider a Gilbert-Elliot
service profile at the server node. In this model, service times at the server
node follow a finite state Markov chain with two states: state and
state where the server is faster in state . We determine the
time average age experienced by the monitor node and characterize the
age-optimal state transition matrix with and without an average cost
constraint on the service operation. Next, we consider a Gilbert-Elliot
sampling profile at the source. In this model, the interarrival times follow a
finite state Markov chain with two states: state and state
where samples are more frequent in state . We find the time average age
experienced by the monitor node and characterize the age-optimal state
transition matrix
Caching under Content Freshness Constraints
Several real-time delay-sensitive applications pose varying degrees of
freshness demands on the requested content. The performance of cache
replacement policies that are agnostic to these demands is likely to be
sub-optimal. Motivated by this concern, in this paper, we study caching
policies under a request arrival process which incorporates user freshness
demands. We consider the performance metric to be the steady-state cache hit
probability. We first provide a universal upper bound on the performance of any
caching policy. We then analytically obtain the content-wise hit-rates for the
Least Popular (LP) policy and provide sufficient conditions for the asymptotic
optimality of cache performance under this policy. Next, we obtain an accurate
approximation for the LRU hit-rates in the regime of large content population.
To this end, we map the characteristic time of a content in the LRU policy to
the classical Coupon Collector's Problem and show that it sharply concentrates
around its mean. Further, we develop modified versions of these policies which
eject cache redundancies present in the form of stale contents. Finally, we
propose a new policy which outperforms the above policies by explicitly using
freshness specifications of user requests to prioritize among the cached
contents. We corroborate our analytical insights with extensive simulations
Cache Updating Strategy Minimizing the Age of Information with Time-Varying Files' Popularities
We consider updating strategies for a local cache which downloads
time-sensitive files from a remote server through a bandwidth-constrained link.
The files are requested randomly from the cache by local users according to a
popularity distribution which varies over time according to a Markov chain
structure. We measure the freshness of the requested time-sensitive files
through their Age of Information (AoI). The goal is then to minimize the
average AoI of all requested files by appropriately designing the local cache's
downloading strategy. To achieve this goal, the original problem is relaxed and
cast into a Constrained Markov Decision Problem (CMDP), which we solve using a
Lagrangian approach and Linear Programming. Inspired by this solution for the
relaxed problem, we propose a practical cache updating strategy that meets all
the constraints of the original problem. Under certain assumptions, the
practical updating strategy is shown to be optimal for the original problem in
the asymptotic regime of a large number of files.
For a finite number of files, we show the gain of our practical updating
strategy over the traditional square-root-law strategy (which is optimal for
fixed non time-varying file popularities) through numerical simulations.Comment: To appear ITW202
Freshness-Optimal Caching for Information Updating Systems with Limited Cache Storage Capacity
In this paper, we investigate a cache updating system with a server
containing files, relays and users. The server keeps the freshest
versions of the files which are updated with fixed rates. Each relay can
download the fresh files from the server in a certain period of time. Each user
can get the fresh files from any relay as long as the relay has stored the
fresh versions of the requested files. Due to the limited storage capacity and
updating capacity of each relay, different cache designs will lead to different
average freshness of all updating files at users. In order to keep the average
freshness as large as possible in the cache updating system, we formulate an
average freshness-optimal cache updating problem (AFOCUP) to obtain an optimal
cache scheme. However, because of the nonlinearity of the AFOCUP, it is
difficult to seek out the optimal cache scheme. As a result, an linear
approximate model is suggested by distributing the total update rates
completely in accordance with the number of files in the relay in advance. Then
we utilize the greedy algorithm to search the optimal cache scheme that is
satisfied with the limited storage capacity of each relay. Finally, some
numerical examples are provided to illustrate the performance of the
approximate solution
Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?
The proliferation of smart mobile devices has spurred an explosive growth of
mobile crowd-learning services, where service providers rely on the user
community to voluntarily collect, report, and share real-time information for a
collection of scattered points of interest. A critical factor affecting the
future large-scale adoption of such mobile crowd-learning applications is the
freshness of the crowd-learned information, which can be measured by a metric
termed ``age-of-information'' (AoI). However, we show that the AoI of mobile
crowd-learning could be arbitrarily bad under selfish users' behaviors if the
system is poorly designed. This motivates us to design efficient reward
mechanisms to incentivize mobile users to report information in time, with the
goal of keeping the AoI and congestion level of each PoI low. Toward this end,
we consider a simple linear AoI-based reward mechanism and analyze its AoI and
congestion performances in terms of price of anarchy (PoA), which characterizes
the degradation of the system efficiency due to selfish behavior of users.
Remarkably, we show that the proposed mechanism achieves the optimal AoI
performance asymptotically in a deterministic scenario. Further, we prove that
the proposed mechanism achieves a bounded PoA in general stochastic cases, and
the bound only depends on system parameters. Particularly, when the service
rates of PoIs are symmetric in stochastic cases, the achieved PoA is
upper-bounded by asymptotically. Collectively, this work advances our
understanding of information freshness in mobile crowd-learning systems
Age of Information Performance of Multiaccess Strategies with Packet Management
We consider a system consisting of source nodes communicating with a
common receiver. Each source node has a buffer of infinite capacity to store
incoming bursty traffic in the form of status updates transmitted in packets,
which should maintain the status information at the receiver fresh. Packets
waiting for transmission can be discarded to avoid wasting network resources
for the transmission of stale information. We investigate the age of
information (AoI) performance of the system under scheduled and random access.
Moreover, we present analysis of the AoI with and without packet management at
the transmission queue of the source nodes, where as packet management we
consider the capability to replace unserved packets at the queue whenever newer
ones arrive. Finally, we provide simulation results that illustrate the impact
of the network operating parameters on the age performance of the different
access protocols