学术英语论文(1)
电视平台-
NANCHANG
UNIVERSITY
课程名称：
学术英语
题
目：
A Study of Energy Efficient _
Cloud Computing Powered by
_Wireless Energy Transfer ___
英语班级：
理工
1615
班
专业
/
年级：
物联网工程
161
班
姓名
/
学号：
(47)
二零一八年六月
A Study
of Energy Efficient Cloud Computing Powered by
Wireless Energy Transfer
A Study of
Energy Efficient Mobile
Cloud
Computing
Powered by Wireless Energy
Transfer
Abstract
Achieving long battery lives or even
self-sustainability has been a long standing
challenge
for designing mobile devices.
This study presents a novel solution that
seamlessly integrates
two
technologies, mobile cloud
computing
and microwave
power transfer
(MPT),
to
enable
computation
in
passive
low-complexity
devices
such
as
sensors
and
wearable
computing
devices. Specifically, considering
a
single-user
system,
a
base
station
(BS)
either
transfers
power
to
or
offloads
computation
from
a
mobile
to
the
cloud;
the
mobile
uses
harvested
energy
to
compute
given
data
either
locally
or
by
offloading.
A
framework
for
energy
efficient computing
is proposed that comprises a set of policies for
controlling CPU cycles for
the mode of
local computing, time division between MPT and
offloading for the other mode
of
offloading,
and
mode
selection.
Given the CPU-cycle statistics
information and channel
state
information
(CSI),
the
policies
aim
at
maximizing
the
probability
of
successfully
computing
given
data,
called
computing
probability,
under
the
energy
harvesting
and
deadline constraints. Furthermore, this
study reveals that the two simple solutions to
achieve the
object to support
computation load allocation over multiple channel
realizations, which further
increases
the computing probability. Last, the two kinds of
modes suggest that the feasibility of
wirelessly
powered
mobile
cloud
computing
and
the
gain
of
its
optimal control.
And
the
future aspect to study
is simply to be answer.
Key
words
:
wireless
power
transfer;
energy
harvesting
communications;
mobile
cloud
computing; energy efficient computing
1
A Study of Energy Efficient Cloud
Computing Powered by Wireless Energy Transfer
Introduction
Mobile cloud computing (MCC) as an
emerging computing paradigm integrates cloud
computing and mobile computing to
enhance the computation performance of mobile
devices.
The objective of MCC is to
extend powerful computing capability of the
resource-rich clouds
to the resource-
constrained mobile devices (e.g., laptop, tablet
and smartphone) so as to reduce
computation time, conserve local
resources, especially battery, and extend storage
capacity. To
achieve this objective,
MCC needs to transfer resource-intensive
computations from mobile
devices to
clouds, referred to as computation offloading. The
core of computation offloading is
to
decide on which computation tasks should be
executed on the mobile device or on the cloud,
and how to schedule local and cloud
resource to implement task offloading.
The explosive
growth of
Internet of Things (IOT) and mobile communication
is leading to the deployment of
tens of
billions of cloud-based mobile sensors and
wearable computing devices in near future
(Huang
&
Chae,
2010).
Prolonging
their
battery
lives
and
enhancing
their
computing
capabilities are
two key design challenges. They can be tackled by
two promising technologies:
microwave
power transfer
(MPT) for
powering the mobiles computation-intensive tasks
from
the mobiles to the cloud
and
mobile
computation
offloading
(MCO). Two technologies are
seamlessly integrated in the current
work to develop a novel design framework for
realizing
wirelessly powered mobile
cloud computing under the criterion of maximizing
the probability
of successfully
computing given data, called computing
probability. The framework is feasible
since MPT has been proven in various
experiments for powering small devices such as
sensors
or even small-scale airplanes
and helicopters. Furthermore, sensors and wearable
computing
devices targeted in the
framework are expected to be connected by the
cloud- based IOT in the
future,
providing a suitable platform for realizing MCO.
Materials
MCO has been an active
research area in computer science where research
has focused
on
designing
mobile-cloud
systems
and
software
architectures,
virtual
machine
migration
design in the cloud and code
partitioning techniques in the mobiles for
reducing the energy
consumption
and
improving
the
computing
performance
of
mobiles.
Nevertheless,
implementation
of
MCO
requires
data
transmission
and
message
passing
over
wireless
channels,
incurring
transmission
power
consumption.
The
existence
of
such
a
tradeoff
has
motivated
cross-disciplinary
research
on
jointly
designing
MCO and adaptive
transmission
algorithms
to
maximize
the
mobile
energy
savings.
A
stochastic
control
algorithm
was
proposed for adapting the offloaded
components of an application to a time-varying
wireless
channel. Furthermore,
multiuser computation offloading in a multi-cell
system was explored
by
Shinohara
(2014), where the
radio and computational resources were jointly
allocated for
maximizing the energy
savings under the latency constraints.
According
to
Swan
(2012), the threshold-based offloading
policy was derived for the
system with intermittent connectivity
between the mobile and cloud.
Lastly, the CPU-cycle
frequencies are jointly controlled with
MCO given a more skilled and increasingly
appropriate
2