IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 108897 - 108920
Published: Jan. 1, 2024
Task
Scheduling
is
a
crucial
challenge
in
cloud
computing
as
diversified
tasks
come
rapidly
onto
console
dynamically
from
heterogeneous
resources
which
consists
of
different
task
lengths,
processing
capacities.
Generating
schedules
for
these
type
Cloud
Service
Provider(CSP).Therefore,
to
generate
paradigm
effectively
by
considering
arising
and
match
it
with
respective
Virtual
Machine
(VM),
scheduler
formulated
using
Deep
Deterministic
Policy
Gradient
(DDPG)
algorithm
used
methodology
design
scheduler.
This
works
three
stages.
In
the
initial
stage,
are
classified
based
on
length
capacity
identify
them
whether
they
High
Performance
Computing
(HPC)
or
Throughput
(HTC)
tasks.
After
classification,
second
be
tracked
matches
corresponding
nature
Finally,
third
according
VM
priorities
calculated
electricity
unit
cost
mapped
VMs.
Simulations
conducted
Cloudsim
fabricated
workload
distributions
realtime
worklogs.
our
proposed
Hybrid
scheduler(HDDPGTS)
evaluated
over
DQN,
A2C
algorithms.
From
results,
proved
that
HDDPGTS
significantly
improved
makespan,
Energy
consumption,
scheduling
overhead,
scalability
baseline
approaches.
Journal of Cloud Computing Advances Systems and Applications,
Journal Year:
2025,
Volume and Issue:
14(1)
Published: Jan. 11, 2025
To
facilitate
flexible
manufacturing,
modern
industries
have
incorporated
numerous
modular
operations
such
as
multi-robot
services
which
can
be
expediently
arranged
or
offloaded
to
other
production
resources.
However,
complex
manufacturing
projects
often
consist
of
multiple
tasks
with
fixed
sequences,
posing
a
significant
challenge
for
smart
factories
in
efficiently
scheduling
limited
robot
resources
complete
specific
tasks.
Additionally,
when
span
across
factories,
ensuring
faithful
execution
contracts
becomes
another
challenge.
In
this
paper,
we
propose
modified
combinatorial
auction
method
combined
blockchain
and
edge
computing
technologies
organize
project
scheduling.
Firstly,
transform
efficient
resource
into
resource-constrained
multi-project
problem
(RCPSP).
Subsequently,
the
solution
integrates
random
sampling
(CA-RS)
contracts.
Alongside
security
analysis,
simulations
are
conducted
using
real
data
sets.
The
results
indicate
that
suggested
CA-RS
approach
significantly
enhances
efficiency
arrangement
within
industrial
Internet
Things
compared
baseline
algorithms.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 20, 2025
The
fast
growth
of
the
Internet
Everything
(IoE)
has
resulted
in
an
exponential
rise
network
data,
increasing
demand
for
distributed
computing.
Data
collection
and
management
with
job
scheduling
using
wireless
sensor
networks
are
considered
essential
requirements
IoE
environment;
however,
security
issues
over
data
on
online
platform
energy
consumption
must
be
addressed.
Secure
Edge
Enabled
Multi-Task
Scheduling
(SEE-MTS)
model
been
suggested
to
properly
allocate
jobs
across
machines
while
considering
availability
relevant
copies.
proposed
approach
leverages
edge
computing
enhance
efficiency
applications,
addressing
growing
need
manage
huge
generated
by
devices.
system
ensures
user
protection
through
dynamic
updates,
multi-key
search
generation,
encryption,
verification
result
accuracy.
A
MTS
mechanism
is
employed
optimize
usage,
which
allocates
slots
various
processing
tasks.
Energy
assessed
tasks
queues,
preventing
node
overloading
minimizing
disruptions.
Additionally,
reinforcement
learning
techniques
applied
reduce
overall
task
completion
time
minimal
data.
Efficiency
have
improved
due
reduced
energy,
delay,
reaction,
times.
Results
indicate
that
SEE-MTS
achieves
utilization
4
J,
a
delay
2s,
reaction
4s,
at
89%,
level
96%.
With
computation
6s,
offers
security,
reducing
times,
although
real-world
implementation
may
limited
number
devices
incoming
International Journal of Computer Networks And Applications,
Journal Year:
2024,
Volume and Issue:
11(6), P. 835 - 854
Published: Dec. 30, 2024
Cloud
computing
infrastructures
are
particularly
vulnerable
to
Distributed
Denial
of
Service
(DDoS)
attacks
due
the
large-scale
and
dynamic
nature
resources.Large
data
volumes
handled
by
cloud
settings,
which
raises
computational
cost
detection,
filtering
malicious
traffic
from
genuine
in
such
large
quantities
is
difficult.The
conventional
detection
techniques
insufficient.The
optimized
Elman
Neural
Network
(ENN)
used
this
study's
proposed
enhanced
DDoS
attack
framework
combines
centroid
opposition-based
learning
(COBL)
with
bacterial
colony
optimization
(BCO)
called
COBCO.The
BCO
lacks
population
diversity
can
fall
into
local
optima
random
initialization
update.To
overcome
above
issues,
COBL
for
update
enhance
avoid
issues.By
imitating
foraging
behavior,
COBCO
algorithm
improves
ENN's
capacity
explore
exploit
solution
space,
increasing
network's
speed
convergence
accuracy
detection.Meanwhile,
enhances
process
producing
a
wider
range
solid
candidate
solutions,
offset
drawbacks
learning.Extensive
simulations
show
that
suggested
strategy
outperforms
traditional
identifying
different
kinds
attacks.
2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI),
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 8
