1,330 research outputs found
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service
End-to-end delay is a critical attribute of quality of service (QoS) in
application domains such as cloud computing and computer networks. This metric
is particularly important in tandem service systems, where the end-to-end
service is provided through a chain of services. Service-rate control is a
common mechanism for providing QoS guarantees in service systems. In this
paper, we introduce a reinforcement learning-based (RL-based) service-rate
controller that provides probabilistic upper-bounds on the end-to-end delay of
the system, while preventing the overuse of service resources. In order to have
a general framework, we use queueing theory to model the service systems.
However, we adopt an RL-based approach to avoid the limitations of
queueing-theoretic methods. In particular, we use Deep Deterministic Policy
Gradient (DDPG) to learn the service rates (action) as a function of the queue
lengths (state) in tandem service systems. In contrast to existing RL-based
methods that quantify their performance by the achieved overall reward, which
could be hard to interpret or even misleading, our proposed controller provides
explicit probabilistic guarantees on the end-to-end delay of the system. The
evaluations are presented for a tandem queueing system with non-exponential
inter-arrival and service times, the results of which validate our controller's
capability in meeting QoS constraints.Comment: 8 pages, Accepted to AAAI 202
Estimation of Missing Data in Intelligent Transportation System
Missing data is a challenge in many applications, including intelligent
transportation systems (ITS). In this paper, we study traffic speed and travel
time estimations in ITS, where portions of the collected data are missing due
to sensor instability and communication errors at collection points. These
practical issues can be remediated by missing data analysis, which are mainly
categorized as either statistical or machine learning(ML)-based approaches.
Statistical methods require the prior probability distribution of the data
which is unknown in our application. Therefore, we focus on an ML-based
approach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes
both temporal and spatial characteristics of the data. We evaluate the
effectiveness of this approach on a TomTom dataset containing spatio-temporal
measurements of average vehicle speed and travel time in the Greater Toronto
Area (GTA). We evaluate the method under various conditions, where the results
demonstrate that M-RNN outperforms existing solutions,e.g., spline
interpolation and matrix completion, by up to 58% decreases in Root Mean Square
Error (RMSE).Comment: presented at the 2020 92nd IEEE conference on vehicular technology,
18 Nov.-16 Dec 2020 6 pages, 5 figures, 2 table
- …