10 research outputs found
Towards the Tradeoff Between Service Performance and Information Freshness
The last decade has witnessed an unprecedented growth in the demand for
data-driven real-time services. These services are fueled by emerging
applications that require rapidly injecting data streams and computing updated
analytics results in real-time. In many of such applications, the computing
resources are often shared for processing both updates from information sources
and queries from end users. This requires joint scheduling of updates and
queries because the service provider needs to make a critical decision upon
receiving a user query: either it responds immediately with currently available
but possibly stale information, or it first processes new updates and then
responds with fresher information. Hence, the tradeoff between service
performance and information freshness naturally arises in this context. To that
end, we propose a simple single-server two-queue model that captures the
coupled scheduling of updates and queries and aim to design scheduling policies
that can properly address the important tradeoff between performance and
freshness. Specifically, we consider the response time as a performance metric
and the Age of Information (AoI) as a freshness metric. After demonstrating the
limitations of the simplest FCFS policy, we propose two threshold-based
policies: the Query-k policy that prioritizes queries and the Update-k policy
that prioritizes updates. Then, we rigorously analyze both the response time
and the Peak AoI (PAoI) of the threshold-based policies. Further, we propose
the Joint-(M,N) policy, which allows flexibly prioritizing updates or queries
through choosing different values of two thresholds M and N. Finally, we
conduct simulations to evaluate the response time and the PAoI of the proposed
policies. The results show that our proposed threshold-based policies can
effectively control the balance between performance and freshness.Comment: Submitted to 2019 IEEE International Conference on Communications
(ICC
Waiting but not Aging: Optimizing Information Freshness Under the Pull Model
The Age-of-Information is an important metric for investigating the
timeliness performance in information-update systems. In this paper, we study
the AoI minimization problem under a new Pull model with replication schemes,
where a user proactively sends a replicated request to multiple servers to
"pull" the information of interest. Interestingly, we find that under this new
Pull model, replication schemes capture a novel tradeoff between different
values of the AoI across the servers (due to the random updating processes) and
different response times across the servers, which can be exploited to minimize
the expected AoI at the user's side. Specifically, assuming Poisson updating
process for the servers and exponentially distributed response time, we derive
a closed-form formula for computing the expected AoI and obtain the optimal
number of responses to wait for to minimize the expected AoI. Then, we extend
our analysis to the setting where the user aims to maximize the AoI-based
utility, which represents the user's satisfaction level with respect to
freshness of the received information. Furthermore, we consider a more
realistic scenario where the user has no prior knowledge of the system. In this
case, we reformulate the utility maximization problem as a stochastic
Multi-Armed Bandit problem with side observations and leverage a special linear
structure of side observations to design learning algorithms with improved
performance guarantees. Finally, we conduct extensive simulations to elucidate
our theoretical results and compare the performance of different algorithms.
Our findings reveal that under the Pull model, waiting does not necessarily
lead to aging; waiting for more than one response can often significantly
reduce the AoI and improve the AoI-based utility in most scenarios.Comment: 15 pages. arXiv admin note: substantial text overlap with
arXiv:1704.0484