Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Engineering a QoS Provider Mechanism for Edge Computing with Deep
  Reinforcement Learning   With the development of new system solutions that integrate traditional cloud
computing with the edge/fog computing paradigm, dynamic optimization of service
execution has become a challenge due to the edge computing resources being more
distributed and dynamic. How to optimize the execution to provide Quality of
Service (QoS) in edge computing depends on both the system architecture and the
resource allocation algorithms in place. We design and develop a QoS provider
mechanism, as an integral component of a fog-to-cloud system, to work in
dynamic scenarios by using deep reinforcement learning. We choose reinforcement
learning since it is particularly well suited for solving problems in dynamic
and adaptive environments where the decision process needs to be frequently
updated. We specifically use a Deep Q-learning algorithm that optimizes QoS by
identifying and blocking devices that potentially cause service disruption due
to dynamicity. We compare the reinforcement learning based solution with
state-of-the-art heuristics that use telemetry data, and analyze pros and cons.

6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ