A graph reinforcement LearningPowered Online-Computational task offloading and latency minimization framework for wireless mobile edge computing networks
Cognitive Systems Research,
Journal Year:
2025,
Volume and Issue:
unknown, P. 101355 - 101355
Published: March 1, 2025
Language: Английский
A GRL-aided federated graph reinforcement learning approach for enhanced file caching in mobile edge computing
Abhinav Khanna,
No information about this author
G Anjali,
No information about this author
Nilesh Kumar Verma
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et al.
Computing,
Journal Year:
2024,
Volume and Issue:
107(1)
Published: Dec. 30, 2024
Language: Английский
Evaluating QoS Performance in Multi-User High Speed Wireless Networks with Adaptive Spectrum Allocation
Published: Dec. 28, 2023
The
technical
information
of
the
paper
attention
at
improvement
and
assessment
an
advanced
adaptive
spectrum
allocation
(ASA)
algorithm
for
multi-consumer
high
pace
wireless
networks.
makes
use
efficient
time-domain
slot
mechanism
to
exploit
traits
modern-day
Wi-Fi
MAC
protocols.
aim
is
improve
first-class
provider
(Qu's)
overall
performance
in
surroundings
by
coordinating
scheduler
radio
get
entry
community
(RAN).
Three
predominant
metrics
are
used
assess
proposed
ASA
set
rules.
primary
a
person
consumer's
spectral
efficiency
which
measures
throughput
system
as
characteristic
quantity
customers.
second
aggregated
that
looks
ability
network
way
it
dispensed
among
ultimate
metric
delay
skilled
means
customers
whilst
having
access
community.
also
addresses
issues
associated
with
channel
sensing,
admission
manage,
premier
scheduling
hyperlinks.
simulation
outcomes
display
plays
higher
than
other
options
just
like
round
robin
FIFO
schemes.
Language: Английский
Mobile-Aware Task Offloading Strategy in Large-Scale Asynchronous Mobile Edge Computing based on a Hybrid Deep Q-learning and an Index Policy Scheme
Jinpeng Wang,
No information about this author
Qingyong Meng,
No information about this author
Yufang Kang
No information about this author
et al.
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 15, 2024
Abstract
Mobile-edge
computing
(MEC)
offloads
computational
tasks
from
wireless
devices
to
network
edge,
and
enables
realtime
information
transmission
computing.
Most
existing
work
concerns
a
small-scale
synchronous
MEC
system.
In
this
paper,
we
focus
on
large-scale
asynchronous
system
with
random
task
arrivals,
distinct
workloads,
diverse
deadlines.
We
formulate
the
offloading
policy
design
as
restless
multi-armed
bandit
(RMAB)
maximize
total
discounted
reward
over
time
horizon.
However,
formulated
RMAB
is
related
PSPACE
hard
sequential
decision-making
problem,
which
intractable.
To
address
issue,
by
exploiting
Whittle
index
(WI)
theory,
rigorously
establish
WI
indexability
derive
scalable
closed
form
solution.
Consequently,
in
our
policy,
each
user
only
needs
calculate
its
report
it
BS,
users
highest
indices
are
selected
for
offloading.
Furthermore,
when
completion
ratio
becomes
focus,
shorter
slack
less
remaining
workload
(STLW)
priority
rule
introduced
into
performance
improvement.
When
knowledge
of
energy
consumption
not
available
prior
offloading,
develop
Bayesian
learning-enabled
policies,
including
maximum
likelihood
estimation,
learning
conjugate
prior,
prior-swapping
techniques.
Simulation
results
show
that
proposed
policies
significantly
outperform
other
policies.
Language: Английский
An Edge Resource Selection Scheme Considering QoS and Computational Parameters
Published: July 29, 2024
Language: Английский
NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks
Future Generation Computer Systems,
Journal Year:
2024,
Volume and Issue:
164, P. 107593 - 107593
Published: Nov. 5, 2024
Language: Английский