12,258 research outputs found
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
A growing demand is witnessed in both industry and academia for employing
Deep Learning (DL) in various domains to solve real-world problems. Deep
Reinforcement Learning (DRL) is the application of DL in the domain of
Reinforcement Learning (RL). Like any software systems, DRL applications can
fail because of faults in their programs. In this paper, we present the first
attempt to categorize faults occurring in DRL programs. We manually analyzed
761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues)
developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl,
Tensorforce) and identified faults reported by developers/users. We labeled and
taxonomized the identified faults through several rounds of discussions. The
resulting taxonomy is validated using an online survey with 19
developers/researchers. To allow for the automatic detection of faults in DRL
programs, we have defined a meta-model of DRL programs and developed DRLinter,
a model-based fault detection approach that leverages static analysis and graph
transformations. The execution flow of DRLinter consists in parsing a DRL
program to generate a model conforming to our meta-model and applying detection
rules on the model to identify faults occurrences. The effectiveness of
DRLinter is evaluated using 15 synthetic DRLprograms in which we injected
faults observed in the analyzed artifacts of the taxonomy. The results show
that DRLinter can successfully detect faults in all synthetic faulty programs
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
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