Journal of Sensors,
Journal Year:
2023,
Volume and Issue:
2023(1)
Published: Jan. 1, 2023
Nowadays,
with
the
advent
of
various
communication
technologies
such
as
internet
things
(IoT),
a
large
volume
data
is
produced
that
needs
to
be
processed
in
real‐time.
Fog
computing
an
appropriate
solution
address
requirements
different
types
IoT
applications.
In
most
cases,
applications
consist
set
dependent
tasks
can
separately
heterogeneous
fog
environment.
Scheduling
these
environment
NP‐hard
problem
vast
amount
time
and
computation
resources
solve,
making
it
infeasible
for
real‐time
addition,
reducing
response
energy
consumption
essential
issue
should
taken
into
account
task
scheduling
algorithms.
To
challenges,
we
aim
propose
multiobjective
model
jointly
improve
efficiency
time.
solve
model,
also
intelligent
named
IETIF
which
combines
leverages
benefits
simulated
annealing
NSGA‐III
Simulation
results
show
outperforms
state‐of‐the‐art
methods
terms
consumption,
time,
speedup.
Telematics and Informatics Reports,
Journal Year:
2023,
Volume and Issue:
10, P. 100049 - 100049
Published: Feb. 27, 2023
Fog
computing
is
a
paradigm
that
utilizes
the
advantages
of
both
cloud
and
edge
devices
providing
quality
services,
reducing
latency,
mobility
support,
multi-tenancy,
many
other
functions
support
modern
systems.
It
sometimes
referred
to
as
fog
networking
or
fogging.
This
paper
reviews
discusses
computing,
briefly
highlighting
implemented
paradigms
before
computing.
These
include
cloud,
mobile
All
targeted
improving
service
between
end
itself.
A
Taxonomy
presented
based
on
contemporary
research
about
security
challenges,
services
issues,
operational
data
management.
The
standard
for
elucidating
taxonomy
built
functional
vital
issues
in
Challenges
potential
applications
are
identified.
review
shows
security,
privacy,
application,
communication
challenges
prominent
among
scholars
contributions.
Potential
also
identified,
including
healthcare
applications,
innovative
city
farm
applications.
Processes,
Journal Year:
2023,
Volume and Issue:
11(4), P. 1162 - 1162
Published: April 10, 2023
Today,
fog
and
cloud
computing
environments
can
be
used
to
further
develop
the
Internet
of
Things
(IoT).
In
such
environments,
task
scheduling
is
very
efficient
for
executing
user
requests,
optimal
IoT
requests
increases
productivity
IoT-fog-cloud
system.
this
paper,
a
hybrid
meta-heuristic
(MH)
algorithm
developed
schedule
in
networks
using
Aquila
Optimizer
(AO)
African
Vultures
Optimization
Algorithm
(AVOA)
called
AO_AVOA.
AO_AVOA,
exploration
phase
AVOA
improved
by
AO
operators
obtain
best
solution
during
process
finding
solution.
A
comparison
between
AO_AVOA
methods
AVOA,
AO,
Firefly
(FA),
particle
swarm
optimization
(PSO),
Harris
Hawks
(HHO)
according
performance
metrics
as
makespan
throughput
shows
high
ability
solve
problem
networks.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 5373 - 5392
Published: Jan. 1, 2024
Workflow
Scheduling
is
a
huge
challenge
in
cloud
paradigm
as
many
number
of
workflows
dynamically
generated
from
various
heterogeneous
resources
and
task
dependencies
each
workflow
varies
other.
Therefore,
if
with
more
not
scheduled
onto
an
appropriate
Virtual
Machine
i.e.
low
processing
capacity
which
leads
to
delay
executing
it
results
increase
makespan,
cost,
energy
consumption.
In
order
effectively
schedule
complex
dependencies,
we
propose
novel
multi
objective
scheduling
algorithm
using
Deep
reinforcement
Learning.
Initially,
priorities
all
calculated
based
on
their
then
VMs
electricity
cost
at
datacenters
map
precise
VMs.
These
are
fed
scheduler
uses
Q-Network
model
tasks
by
considering
both
Extensive
simulations
carried
out
workflowsim
realtime
scientific
(Montage,
cybershake,
Epigenomics,
LIGO).
Our
proposed
MOPWSDRL
compared
against
existing
state
art
approaches
Heterogeneous
Earliest
First
Deadline,
Cat
Swarm
Optimization,
Ant
Colony
Optimization.
Results
revealed
that
our
MOPDSWRL
outperforms
algorithms
minimizing
PeerJ Computer Science,
Journal Year:
2024,
Volume and Issue:
10, P. e2128 - e2128
Published: June 17, 2024
Fog
computing
has
emerged
as
a
prospective
paradigm
to
address
the
computational
requirements
of
IoT
applications,
extending
capabilities
cloud
network
edge.
Task
scheduling
is
pivotal
in
enhancing
energy
efficiency,
optimizing
resource
utilization
and
ensuring
timely
execution
tasks
within
fog
environments.
This
article
presents
comprehensive
review
advancements
task
methodologies
for
systems,
covering
priority-based,
greedy
heuristics,
metaheuristics,
learning-based,
hybrid
nature-inspired
heuristic
approaches.
Through
systematic
analysis
relevant
literature,
we
highlight
strengths
limitations
each
approach
identify
key
challenges
facing
scheduling,
including
dynamic
environments,
heterogeneity,
scalability,
constraints,
security
concerns,
algorithm
transparency.
Furthermore,
propose
future
research
directions
these
challenges,
integration
machine
learning
techniques
real-time
adaptation,
leveraging
federated
collaborative
developing
resource-aware
energy-efficient
algorithms,
incorporating
security-aware
techniques,
advancing
explainable
AI
methodologies.
By
addressing
pursuing
directions,
aim
facilitate
development
more
robust,
adaptable,
efficient
task-scheduling
solutions
ultimately
fostering
trust,
security,
sustainability
systems
facilitating
their
widespread
adoption
across
diverse
applications
domains.
