IEEE Access,
Journal Year:
2022,
Volume and Issue:
10, P. 84579 - 84621
Published: Jan. 1, 2022
Internet
of
Things
(IoT)
services
have
grown
substantially
in
recent
years.
Consequently,
IoT
service
providers
(SPs)
are
emerging
the
market
and
competing
to
offer
their
services.
Many
applications
utilize
these
an
integrated
manner
with
different
Quality-of-Service
(QoS)
requirements.
Thus,
provisioning
end-to-end
QoS
is
getting
more
indispensable
for
platforms.
However,
system
by
using
only
metrics
without
considering
user
experiences
not
sufficient.
Recently,
Quality
Experience
(QoE)
model
has
become
a
promising
approach
quantify
actual
A
holistic
design
that
considers
constraints
various
QoS/QoE
together
needed
satisfy
requirements
Besides,
may
operate
environments
limited
resources.
Therefore,
effective
management
resources
essential
support.
This
paper
provides
comprehensive
survey
state-of-the-art
studies
on
perspective.
Our
contributions
threefold:
(1)
QoE-driven
architecture
demonstrated
classifying
vital
components
according
QoE-related
functions
prior
studies,
(2)
QoE
optimization
objectives
classified
corresponding
resource
control
problems
architecture,
(3)
QoE-aware
e.g.,
offloading,
placement
data
caching
policies
Machine
Learning
approaches
extensively
reviewed.
Mobile Information Systems,
Journal Year:
2022,
Volume and Issue:
2022, P. 1 - 17
Published: June 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,
Journal Year:
2023,
Volume and Issue:
56(1), P. 1 - 41
Published: June 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,
Journal Year:
2023,
Volume and Issue:
54(1), P. 3 - 23
Published: July 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.
