Grid-connected desalination plant economic management powered by renewable resources utilizing Niching Chimp Optimization and hunger game search algorithms
Sustainable Computing Informatics and Systems,
Год журнала:
2024,
Номер
42, С. 100976 - 100976
Опубликована: Фев. 2, 2024
Язык: Английский
A Comparative Analysis of Metaheuristic Techniques for High Availability Systems
IEEE Access,
Год журнала:
2024,
Номер
12, С. 7382 - 7398
Опубликована: Янв. 1, 2024
In
the
ever-evolving
technological
landscape,
ensuring
high
system
availability
has
become
a
paramount
concern.
This
research
paper
focuses
on
cloud
computing,
domain
witnessing
exponential
growth
and
emerging
as
critical
use
case
for
high-availability
systems.
To
fulfil
criteria,
many
services
in
infrastructures
should
be
combined,
relying
user's
demands.
Central
to
this
study
is
load
balancing,
an
integral
element
harnessing
full
potential
of
heterogeneous
computing
environments,
dynamic
management
balancing
crucial.
explores
how
virtual
machines
can
effectively
remap
resources
response
fluctuating
loads
dynamically,
optimizing
overall
network
performance.
The
core
involves
in-depth
analysis
several
metaheuristic
algorithms
applied
computing.
These
include
Genetic
Algorithm,
Particle
Swarm
Optimization,
Ant
Colony
Artificial
Bee
Colony,
Grey
Wolf
Optimization.
Utilizing
CloudAnalyst,
conducts
comparative
these
techniques,
focusing
key
performance
metrics
such
Total
Response
Time
(TRT)
Data
Center
Processing
(DCPT).
findings
offer
insights
into
varying
behaviors
under
different
configurations
user
retention
levels.
ultimate
aim
pave
way
developing
innovative
load-balancing
strategies
By
providing
comprehensive
evaluation
existing
methods,
contributes
advancing
systems,
underscoring
importance
tailored
solutions
realm
technology.
Язык: Английский
Optimizing Cloud Resource Management with an IoT-enabled Optimized Virtual Machine Migration Scheme for Improved Efficiency
Journal of Network and Computer Applications,
Год журнала:
2025,
Номер
unknown, С. 104137 - 104137
Опубликована: Фев. 1, 2025
Язык: Английский
Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms
Intelligent Automation & Soft Computing,
Год журнала:
2024,
Номер
39(3), С. 437 - 476
Опубликована: Янв. 1, 2024
Cloud
Computing
has
the
ability
to
provide
on-demand
access
a
shared
resource
pool.It
completely
changed
way
businesses
are
managed,
implement
applications,
and
services.The
rise
in
popularity
led
significant
increase
user
demand
for
services.However,
cloud
environments
efficient
load
balancing
is
essential
ensure
optimal
performance
utilization.This
systematic
review
targets
detailed
description
of
techniques
including
static
dynamic
algorithms.Specifically,
metaheuristic-based
algorithms
identified
as
solution
case
increased
traffic.In
cloud-based
context,
this
paper
describes
measurements,
benefits
drawbacks
associated
with
selected
techniques.It
also
summarizes
based
on
implementation,
time
complexity,
adaptability,
issue(s),
targeted
QoS
parameters.Additionally,
analysis
evaluates
tools
instruments
utilized
each
investigated
study.Moreover,
comparative
among
static,
traditional
metaheuristic
response
by
using
CloudSim
simulation
tool
performed.Finally,
key
open
problems
potential
directions
state-of-the-art
approaches
addressed.
Язык: Английский
Classification of Load Balancing Optimization Algorithms in Cloud Computing: A Survey Based on Methodology
Wireless Personal Communications,
Год журнала:
2024,
Номер
136(4), С. 2069 - 2103
Опубликована: Июнь 1, 2024
Язык: Английский
Hybrid Markov Chain-Based Dynamic Scheduling to Improve Load Balancing Performance in Fog-Cloud Environment
Sustainable Computing Informatics and Systems,
Год журнала:
2024,
Номер
unknown, С. 101077 - 101077
Опубликована: Дек. 1, 2024
Язык: Английский
Secure data transmission in cloud computing using a cyber-security trust model with multi-risk protection scheme in smart IOT application
Torana Kamble,
Madhuri Ghuge,
Ritu Jain
и другие.
Cluster Computing,
Год журнала:
2024,
Номер
28(2)
Опубликована: Ноя. 26, 2024
Язык: Английский
Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework
Heliyon,
Год журнала:
2024,
Номер
10(11), С. e31622 - e31622
Опубликована: Май 23, 2024
In
today's
increasingly
popular
Internet
of
Things
(IoT)
technology,
its
energy
consumption
issue
is
also
becoming
prominent.
Currently,
the
application
Mobile
Edge
Computing
(MEC)
in
IoT
important,
and
scheduling
tasks
to
save
imperative.
To
address
aforementioned
issues,
we
propose
a
Multi-User
Multi-Server
(MUMS)
framework
aimed
at
reducing
MEC.
The
starts
with
model
definition
phase,
detailing
multi-user
multi-server
systems
through
four
fundamental
models:
communication,
offloading,
energy,
delay.
Then,
these
models
are
integrated
construct
an
optimization
for
MUMS.
final
step
involves
utilizing
proposed
L1_PSO
(an
enhanced
version
standard
particle
swarm
algorithm)
solve
problem.
Experimental
results
demonstrate
that,
compared
typical
algorithms,
MUMS
both
reasonable
feasible.
Notably,
algorithm
reduces
by
4.6%
Random
Assignment
2.3%
conventional
Particle
Swarm
Optimization
algorithm.
Язык: Английский
Analyzing the theoretical merits of Loxi load balancer for improving the efficiency of load balancing in 5G‐edge IoT applications based on Kubernetes
R. Vijayakumar,
Manisha Mali,
Sonali A. Patil
и другие.
Internet Technology Letters,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 29, 2024
Abstract
Load
balancing,
a
critical
aspect
of
cloud
and
cloud‐based
applications,
is
major
challenge
that
demands
our
attention.
Due
to
the
increasing
dynamic
workloads,
load
balancing
becomes
more
important
in
cloud.
One
hyperscale
models
stands
out
for
its
ability
efficiently
balance
by
scaling
allocating
resources
Loxi‐Load‐Balancer
(LLB).
This
paper
explores
explicitly
LLB's
application
context
5G‐Edge
IoT
applications
based
on
Kubernetes.
unique
features,
such
as
open‐source
nature
cloud‐native
loads,
use
eBPF
core
engine
avoid
adding
additional
software
modules
configure
kernel,
change
services
using
existing
layers,
set
it
apart
from
other
balancers.
These
features
provide
high
security,
observability,
networking.
delves
into
how
LLB
used
Kubernetes
increase
speed
flexibility
customizable
services.
automates
all
internal
external
administrations
concerning
monitoring,
deployment,
scaling,
migration,
routing,
configuration,
resource
allocation.
focused
developing
an
efficient
allocation
management
system
Loxi‐Load‐Balancer‐extended
Berkeley
Packet
Filter
(LLB‐eBPF).
Detailed
information
about
LLB‐eBPF‐Kubernetes
given
this
help
you
understand
basics
LLB,
eBPF,
Язык: Английский