Multi-access
Edge
Computing
(MEC)
is
an
emerging
computing
paradigm
that
provides
abundant
resource
support
for
the
next
generation
of
Internet
Things
(IoT).
When
users
move
between
edge
servers
with
limited
coverage,
it
necessary
to
determine
service
migration
strategies
ensure
quality.
However,
traditional
research
on
often
oversimplifies
multi-user
scenario,
focusing
only
individual
users.
This
limitation
hinders
these
methods
from
achieving
optimality
in
real
MEC
environments.
To
address
this
issue,
paper
models
scenario
and
focuses
exploring
impact
user
latency
system
energy
consumption.
We
propose
a
method
based
discrete
version
Soft
Actor-Critic
algorithm
(SACDM).
Through
simulation
experiments
evaluate
performance
our
proposed
solution,
results
demonstrate
outperforms
Deep
Q-learning
(DQNM)
by
reducing
approximately
11.12%.
Additionally,
also
achieves
reduction
consumption
6.66%
compared
DQNM.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 94673 - 94689
Published: Jan. 1, 2024
Cloud
computing
has
been
a
prominent
technology
that
allows
users
to
store
their
data
and
outsource
intensive
computations.
However,
of
cloud
services
are
also
concerned
about
protecting
the
confidentiality
against
attacks
can
leak
sensitive
information.
Although
traditional
cryptography
be
used
protect
static
or
being
transmitted
over
network,
it
does
not
support
processing
encrypted
data.
Homomorphic
encryption
allow
directly
on
data,
but
dishonest
provider
alter
computations
performed,
thus
violating
integrity
results.
To
overcome
these
issues,
we
propose
PEEV
(Parse,
Encrypt,
Execute,
Verify),
framework
developer
with
no
background
in
write
programs
operating
remote
server,
verify
correctness
The
proposed
relies
homomorphic
techniques
as
well
zero-knowledge
proofs
achieve
verifiable
privacy-preserving
computation.
It
supports
practical
deployments
low
performance
overheads
developers
express
high-level
language,
abstracting
away
complexities
verification.
