Automatika,
Год журнала:
2024,
Номер
65(3), С. 973 - 982
Опубликована: Март 12, 2024
Large
amounts
of
processing
resources
are
required
for
the
sensed
raw
big
data
during
generation
process.
Furthermore,
as
typically
privacy
sensitive,
blockchain
technology
can
be
used
to
ensure
concerns.
This
study
examines
a
multiuser
mobile
offloading
network
that
consists
cloud
server
located
remotely
and
an
edge
node.
We
formulate
problem
joint
optimization
task
decision
making
all
users,
computation
resource
allocation
among
executing
applications,
radio
assignment
remote-processing
applications.
The
goal
is
minimize
maximum
weighted
cost
users.
When
compared
other
benchmark
approaches,
simulation
results
show
proposed
algorithm
achieves
optimal
in
terms
both
energy
consumption
delay
result
collaboration.
Finally
strategy
with
93%
efficiency
obtained.
Mobile Information Systems,
Год журнала:
2022,
Номер
2022, С. 1 - 17
Опубликована: Июнь 28, 2022
Cloud
and
mobile
edge
computing
(MEC)
provides
a
wide
range
of
services
for
applications.
In
particular,
enables
storage
infrastructure
provisioned
closely
to
the
end-users
at
cellular
network.
The
small
base
stations
are
deployed
establish
network
that
can
be
coined
with
cloud
infrastructure.
A
large
number
enterprises
individuals
rely
on
offered
by
clouds
meet
their
computational
demands.
Based
user
behavior
demand,
tasks
first
offloaded
from
users
then
executed
one
or
several
specific
in
MEC
architecture
has
capability
handle
devices
turn
generate
high
volumes
traffic.
this
work,
we
provide
holistic
overview
MCC/MEC
technology
includes
background
evolution
remote
computation
technologies.
Then,
main
part
paper
surveys
up-to-date
research
concepts
offloading
mechanisms,
granularities,
techniques.
Furthermore,
discuss
mechanism
static
dynamic
environment
along
optimization
We
further
challenges
potential
future
directions
research.
ACM Computing Surveys,
Год журнала:
2023,
Номер
56(1), С. 1 - 41
Опубликована: Июнь 9, 2023
Today,
cloud
computation
offloading
may
not
be
an
appropriate
solution
for
delay-sensitive
applications
due
to
the
long
distance
between
end-devices
and
remote
datacenters.
In
addition,
a
can
consume
bandwidth
dramatically
increase
costs.
However,
such
as
sensors,
cameras,
smartphones
have
limited
computing
storage
capacity.
Processing
tasks
on
battery-powered
energy-constrained
devices
becomes
even
more
complex.
To
address
these
challenges,
new
paradigm
called
Edge
Computing
(EC)
emerged
nearly
decade
ago
bring
resources
closer
end-devices.
Here,
edge
servers
located
end-device
perform
user
tasks.
Recently,
several
paradigms
Mobile
(MEC)
Fog
(FC)
complement
Cloud
(CC)
EC.
Although
are
heterogeneous,
they
further
reduce
energy
consumption
task
response
time,
especially
applications.
Computation
is
multi-objective,
NP-hard
optimization
problem.
A
significant
part
of
previous
research
in
this
field
devoted
Machine
Learning
(ML)
methods.
One
essential
types
ML
Reinforcement
(RL),
which
agent
learns
how
make
best
decision
using
experiences
gained
from
environment.
This
article
provides
systematic
review
widely
used
RL
approaches
offloading.
It
covers
complementary
mobile
computing,
fog
Internet
Things.
We
explain
reasons
various
methods
technical
point
view.
analysis
includes
both
binary
partial
techniques.
For
each
method,
elements
characteristics
environment
discussed
regarding
most
important
criteria.
Research
challenges
Future
trends
also
mentioned.
Software Practice and Experience,
Год журнала:
2023,
Номер
54(1), С. 3 - 23
Опубликована: Июль 18, 2023
Abstract
Recent
developments
in
the
Internet
of
Things
(IoT)
and
real‐time
applications,
have
led
to
unprecedented
growth
connected
devices
their
generated
data.
Traditionally,
this
sensor
data
is
transferred
processed
at
cloud,
control
signals
are
sent
back
relevant
actuators,
as
part
IoT
applications.
This
cloud‐centric
model,
resulted
increased
latencies
network
load,
compromised
privacy.
To
address
these
problems,
Fog
Computing
was
coined
by
Cisco
2012,
a
decade
ago,
which
utilizes
proximal
computational
resources
for
processing
Ever
since
its
proposal,
fog
computing
has
attracted
significant
attention
research
fraternity
focused
addressing
different
challenges
such
frameworks,
simulators,
resource
management,
placement
strategies,
quality
service
aspects,
economics
so
forth.
However,
after
research,
we
still
do
not
see
large‐scale
deployments
public/private
networks,
can
be
utilized
realizing
interesting
In
literature,
only
pilot
case
studies
small‐scale
testbeds,
utilization
simulators
demonstrating
scale
specified
models
respective
technical
challenges.
There
several
reasons
this,
most
importantly,
did
present
clear
business
companies
participating
individuals
yet.
article
summarizes
technical,
non‐functional,
economic
challenges,
been
posing
hurdles
adopting
computing,
consolidating
them
across
clusters.
The
also
academic
industrial
contributions
provides
future
directions
considering
emerging
trends
federated
learning
quantum
computing.
Applied Sciences,
Год журнала:
2022,
Номер
12(7), С. 3312 - 3312
Опубликована: Март 24, 2022
In
mobile
edge
computing
(MEC),
devices
limited
to
computation
and
memory
resources
offload
compute-intensive
tasks
nearby
servers.
User
movement
causes
frequent
handovers
in
5G
urban
networks.
The
resultant
delays
task
execution
due
unknown
user
position
base
station
lead
increased
energy
consumption
resource
wastage.
current
MEC
offloading
solutions
separate
from
mobility.
For
offloading,
techniques
that
predict
the
user’s
future
location
do
not
consider
direction.
We
propose
a
framework
termed
COME-UP
Computation
Offloading
with
Long-short
term
(LSTM)
based
direction
prediction.
nature
of
mobility
data
is
nonlinear
leads
time
series
prediction
problem.
LSTM
considers
previous
features,
such
as
location,
velocity,
direction,
input
feed-forward
mechanism
train
learning
model
next
location.
proposed
architecture
also
uses
fitness
function
calculate
priority
weights
for
selecting
an
optimum
server
on
latency,
energy,
load.
simulation
results
show
latency
are
lower
than
baseline
techniques,
while
utilization
enhanced.
