Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments
Abstract
Efficient
scheduling
of
scientific
workflows
in
cloud
computing
environments
is
essential
for
optimizing
resource
utilization
and
minimizing
completion
time.
In
this
study,
we
comprehensively
evaluate
different
algorithms,
focusing
on
the
Modified
Firefly
Optimization
Algorithm
(ModFOA)
comparison
with
existing
methods
like
Ant
Colony
(ACO),
Genetic
(GA),
Particle
Swarm
(PSO).
Our
investigation
considers
key
performance
metrics
such
as
makespan,
utilization,
energy
consumption
across
diverse
configurations
scenarios.
Scientific
often
involve
intricate
tasks
dependencies,
posing
challenges
efficient
scheduling.
While
algorithms
have
shown
promise,
they
may
not
fully
address
unique
requirements
environments,
leading
to
suboptimal
outcomes.
Therefore,
propose
evaluating
ModFOA’s
effectiveness
cloud.
Through
comparative
analysis,
ModFOA
demonstrates
superior
terms
achieving
lower
times
various
configurations.
Additionally,
exhibits
competitive
moderate
consumption,
positioning
it
a
promising
solution
workflow
environments.
This
study
underscores
significance
selecting
highlights
potential
improving
management
Further
research
could
focus
refining
parameters
validating
its
practicality
real-world
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Авг. 8, 2024
Язык: Английский