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A User-Satisfaction Based Offloading Technique for Smart City Applications

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The Smart cities applications are gaining an increas-ing interest among administrations, citizens and technologists for their suitability in managing the everyday life. One of the major challenges is regarding the possibility of managing in an efficient way the presence of multiple applications in a Wireless Heterogeneous Network (HetNet) environment, alongside the presence of a Mobile Cloud Computing (MCC) infrastructure. In this context we propose a utility function model derived from the economic world aiming to measure the Quality of Service (QoS), in order to choose the best access point in a HetNet to offload part of an application on the MCC, aiming to save energy for the Smart Mobile Devices (SMDs) and to reduce computational time. We distinguish three different types of application, considering different offloading percentage of computation and analyzing how the cell association algorithm allows energy saving and shortens computation time. The results show that when the network is overloaded, the proposed utility function allows to respect the target values by achieving higher throughput values, and reducing the energy consumption and the computational time.
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A User-Satisfaction Based Offloading Technique for
Smart City Applications
Daniela Mazza, Daniele Tarchi, and Giovanni E. Corazza
Department of Electrical, Electronic and Information Engineering
University of Bologna
40136 Bologna, Italy
email: {daniela.mazza6,daniele.tarchi,giovanni.corazza}@unibo.it
Abstract—The Smart cities applications are gaining an increas-
ing interest among administrations, citizens and technologists
for their suitability in managing the everyday life. One of the
major challenges is regarding the possibility of managing in an
efficient way the presence of multiple applications in a Wireless
Heterogeneous Network (HetNet) environment, alongside the
presence of a Mobile Cloud Computing (MCC) infrastructure. In
this context we propose a utility function model derived from the
economic world aiming to measure the Quality of Service (QoS),
in order to choose the best access point in a HetNet to offload
part of an application on the MCC, aiming to save energy for the
Smart Mobile Devices (SMDs) and to reduce computational time.
We distinguish three different types of application, considering
different offloading percentage of computation and analyzing how
the cell association algorithm allows energy saving and shortens
computation time. The results show that when the network
is overloaded, the proposed utility function allows to respect
the target values by achieving higher throughput values, and
reducing the energy consumption and the computational time.
I. INT ROD UC TI ON
Wireless communications applied to smart cities received a
major boost thanks to new user’s needs, as well as a rapid
growth and diffusion of wireless technologies. To achieve
the goal of interacting with city services, allowing to sim-
plify every-day life, Mobile Cloud Computing (MCC) and
Heterogeneous Networks (HetNets) are considered together
key solutions for the major facing problems: the former for
offloading application to powerful remote servers, shortening
execution time and extending battery life of mobile devices,
the latter for exploiting high-speed and stable connectivity in
an ever grown mobile traffic trend, allowing the use of small
cells in addition to macrocells [1]. In such a scenario users
can access to remote resources without interruption in time
and space.
In this context energy saving and performance improve-
ment of Smart Mobile Devices (SMDs) have been widely
recognized as primary issues. In fact the execution of every
complex application is a big challenge due to the limited
battery power and computation capacity of the mobile de-
vices [2]. The distributed execution (i.e., computation/code
offloading) between the cloud and mobile devices has been
widely investigated [3], highlighting the challenges towards
a more efficient cloud-based offloading framework and also
suggesting some opportunities that may be exploited. Indeed,
the joint optimization of HetNets and distributed processing is
a promising research trend [4]. We envision that the success
of HetNets, jointly with MCC, would ultimately depends on
user satisfaction, which in turn relies on saving energy and
computing application quickly. Identifying the relevant Quality
of Service (QoS) for each of the diverse application types and
distinguishing the variation of user satisfaction related to the
QoS is a research challenge [5].
Various mobile data offloading policies are proposed in the
literature where the partial offloading of data to the network
infrastructure is performed according to the variations of the
network conditions and the operator strategies [6]; however, a
model related to user satisfaction regarding battery saving and
speed of computation is not already taken in consideration.
In this paper we propose a utility function model that takes
into account a series of parameters related to HetNet’s nodes,
SMDs’ characteristics and types of performed application. We
categorize applications in different classes, and consider for
each application type the amount of data and computation
transferred to the MCC and how the HetNet traffic load affects
the power consumption of the SMDs and their execution
time. The opportunity to move to the cloud a portion of
the computing application is taken into account, because the
decision whether moving the computation tasks of mobile
applications from the local SMDs to the remote cloud involves
a tradeoff between energy consumption and computational
time [7].
The proposed utility function takes into account the QoS
parameters in terms of throughput, amount of energy used
by the SMDs and time spent to execute the application. The
proposed utility function acts as input for a cell association
procedure aiming to select the best access point for respecting
the system requirements. The user-satisfaction cell association
algorithm is compared with a legacy algorithm that foresees
the connection with the nearest access point. The results show
that when the network is overloaded, the algorithm based on
the proposed utility function offers a better service with respect
to the nearest-node technique, since the average throughput
is stable, allowing less outage of connectivity and reduced
values of average energy and average time in comparison to
the nearest-node algorithm.
The remainder of the paper is organized as follows. In
Section II, the system model is introduced by focusing on
the joint MCC and HetNet architecture. In Section III, the
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Fig. 1. The reference scenario with different node types in HetNet for
offloading different types of application on MCC
proposed utility function is introduced aiming to optimize the
cell association in the given scenario, while in Section IV, the
numerical results of the proposed approach have been derived.
Finally in Section V, the conclusions are drawn.
II. SY ST EM MO DE L
The reference scenario we are focusing on is characterized
by an urban area with a pervasive wireless coverage, where
several mobile devices are interacting with a traditional cen-
tralized cloud service and request for services from a remote
data center, as illustrated in Fig. 1. In order to connect to the
cloud and the data centers we consider the presence of two
types of Radio Access Technologies (RATs) that compose the
basic elements of the HetNet: macrocells and small cells.
Macrocells - The distance between the access points (base
stations of the macrocells) is usually higher than 500 m.
Thanks to this type of base stations the environment is
completely covered and devices can move by minimizing
the handover frequency. On the other hand, in macrocells
the system suffers for channel fading and traffic conges-
tion. This leads to a lack of stability, not allowing to reach
very high data rate. The technology used for this type of
cells refers to the cellular networks, e.g., 3G, LTE.
Small Cells - Small cells are characterized by low power
radio access nodes, with a coverage range up to 100-
200 m. We can distinguish between Picocells (to provide
typically hotspot coverage in public places as malls,
airports and stadiums without limits for number of con-
nected devices) and Femtocells (to cover a home or small
business, available only for selected devices). Picocells
and Femtocells have been recently introduced as a way
for increasing the coverage and maximize the resource
allocation in LTE networks. We also consider WiFi access
points as nodes with a small coverage range (less than
100 m) which can typically communicate with a reduced
number of client devices. However, the actual range of
communication can vary significantly, depending on such
variables as indoor or outdoor placement, the current
weather, operating radio frequency, and the power output
of devices.
Alongside the presence of a pervasive wireless network, a
smart city environment is characterized by the presence of
sensing and user terminals that generate and exploit a large
amount of data. These data, in order to be user friendly,
need to be elaborated by some centralized or distributed data
centers. If on one side the centralized approach allows to
exploit high performance computing centers, the distributed
approach, residing in high performance smartphones and user
terminals, needs to face with the problem of a lower computing
power and, in particular, with the energy issues of the mobile
devices.
In our scenario we suppose that the computation of a
certain application requires Ooperations. Smd and Scs are,
respectively, the speeds in operations per second of the mobile
device and the cloud server. Hence, a certain application can
be completed in an amount of time Tmd equal to O/Smd on
the device and Tcs equal to O/Scs on the cloud server. On the
other hand, let us suppose that Dcorresponds to the amount
of data that the device has to send to the cloud server for the
remote computation, and Str is the transmission speed, in bit
per second, between the SMD and the access point; hence,
the transmission of data lasts an amount of time Ttr equal to
D/Str . In this case we suppose that the transmission time is
mostly due to the access network transfer, by considering as
negligible the transfer time on the backbone network between
the access point and the cloud server due to the higher data
rate. Moreover, we consider as negligible the return time from
the cloud server to the user terminal because the amount of
data in response to the elaboration in the cloud server is small
with respect to the data sent toward the cloud server [8], [9].
In order to analyze the impact of offloading a portion of
the application to a cloud server, we introduce two weight
coefficients γand δ, satisfying 0γ, δ 1, representing,
respectively, the fraction of the computational task and the
fraction of the data sent for the offloading. In a preliminary
work of the same authors [7] the values γand δhave been
optimized; herein, we consider such values for each selected
application.
In the following we will focus on three application types,
characterized by a specific amount of required operations Ok,
amount of data that need to be exchanged Dk, fraction of
offloaded computational tasks γk, and fraction of offloaded
data δk, as defined in Tab. I. The considered application classes
are:
1) Real time road traffic analysis: the applications aiming
to optimize the route toward a certain destination (e.g.,
navigation applications);
2) Mobile Video and Audio Communications: the appli-
cations that elaborates user generated audio and video
content;
3) Mobile Social Networking: the applications used for
social networking.
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TABLE I
VALUE S AN D FRA CTI ON O F COM PU TAT ION A ND T RAN SM ISS IO N
Application OkγkDk[b]δk
1 - Real time road traf-
fic analysis
1070.9 1050.25
2 - Mobile Video and
Audio Communication
1050.1 1070.07
3 - Mobile Social Net-
working
1070.7 1070.35
Let us focus now on the network infrastructure, by consider-
ing a generic couple composed by the i-th access point and the
j-th SMD; it is possible to derive the associated throughput,
consumed energy and time spent for the computation in the
following way.
a) Throughput Str,ij :The throughput Str,ij is affected by
the bandwidth BWiof the i-th access point, by the distance dij
between the access point and the SMD, by the Signal to Noise
Ratio SNRiat the receiver, and by the number of devices ni
already connected to the i-th access point. By resorting to the
Shannon Formula, the throughput Str,ij can be written as:
Str,ij =BWi
ni
·log2 1 + SNRi
d2
ij !(1)
b) Energy Epart od,ijk:The energy spent for partially
offloading the application can be written as the sum of the
energy spent to perform locally a part of the task and the
energy spent during the idle period and during the transmission
of the remaining part of the task to the cloud; the idle period
corresponds to the amount of time needed by the cloud to
perform the computation: we suppose that during this time
the SMD remains in an idle state. In this case it is possible to
derive the overall spent energy for the k-th application as:
Epart od,ijk =Pl,j ×(1 γk)·Ok
Smd,j
+Pid,j ×γk·Ok
Scs
+Ptr,ij ×δk·Dk
Str,ij
(2)
where Pl,j corresponds to the power consumption for per-
forming the local computation by the j-th SMD, Pid,j is the
power consumption of the j-th SMD in idle state, Ptr,ij is the
power consumption of the j-th SMD for transmitting the data
to the i-th access point, and Smd,j is the computing speed in
operations per second of the j-th SMD.
c) Time Tpart od,ijk:The computation time for executing
the application can be written as the maximum value between
the time needed to compute the local portion of the task and
the time needed for the offloaded portion; we have supposed
that the two phases can be performed at the same time, so that
the overall time corresponds to the maximum value:
Tpart od,ijk = max (1 γk)·Ok
Smd,j
;γk·Ok
Scs
+δk·Dk
Str,ij (3)
III. USE R-SATI SFAC TI ON BA SE D UTI LI TY FU NC TI ON
We introduce, for each of the three quality parameters
taken in consideration, a function representing the QoS degree
perceived by the user. The functions are modeled as sigmoid
curves, since they are well-known functions often used to
describe QoS perception [5], [10], [11]. A sigmoid curve can
be defined as:
U(x) = 1
1 + eα(xβ)(4)
where αand βdecide the steepness and the center of the
curve. The value of αindicates user’s sensitivity to the
QoS degradation while βindicates the acceptable region of
operation. The derivative of the sigmoid function describes the
subject perception, so that it does not make sense to give more
resources over a certain value above which the derivative of
the utility function approximates to zero.
Focusing on the three QoS parameters we are taking into
account, i.e., throughput, energy and time, it is possible to
define the related sigmoid functions, by taking into account
that the user satisfaction grows with higher throughput values
and lower energy and time values. To this aim, concerning the
user throughput, it is possible to define the related sigmoid
function as:
f1(Str,ij) = 1
1 + eα1(Str,ijStro,k )(5)
where Stro,k is the objective throughput value for the k-th
application.
On the other hand, since the energy and time parameters
need to decrease for increasing the user satisfaction, the related
cost functions need to be decreasing sigmoid functions defined
as:
f2(Epart od,ijk) = 1 1
1 + eα2(Epart od,ijkEo,k )(6)
f3(Tpart od,ijk) = 1 1
1 + eα3(Tpart od,ijkTo,k )(7)
The parameter αq(q= 1,2,3) is the steepness of fqand is
related to the user’s sensitivity to the degradation of the QoS
of the q-th parameter. The parameters Stro,k ,Eo,k and To,k are
the center points of the curves fq, indicating the acceptable
region of operation. Stro,k and To,k are reference values for the
data transmission rate and the computing time related to the
type of application requested, whereas Eo,k, the energy spent
to compute le application locally, is also associated to the type
of device in addition to the type of application. For the goal
of this study, referring to network analysis rather than SMD’s
types analysis, we considered values of Eo,k dependent only to
application types. The values of Stro,k,Eo,k and To,k in relation
with the application classes are defined in Tab. II.
On the basis of the QoS sigmoid functions, we introduce
a model developed from the economic concept of utility
function [10]. By focusing on the access point i(related to
the type of RAT) and the SMD j, the cost function for the
association of the j-th SMD to the i-th access point is given
by:
Uij =c1·f1(Str,ij) +c2·f2(Epart od,ijk) + c3·f3(Tpart od,ijk )(8)
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TABLE II
REF ERE NC E VALUE S FOR QOSF UN CTI ON S
Application Stro,k(kb/s)Eo,k (W·s)To,k(s)
1 - Real time road traf-
fic analysis
0.52 2.9 0.5
2 - Mobile Video and
Audio Communication
1.42 3.6 0.1
3 - Mobile Social Net-
working
0.93 7.1 2
TABLE III
WEI GHT PA RAM ET ERS O F UTI LIT Y FUNC TI ON
Application c1c2c3
1 - Real time road traf-
fic analysis
0.6 0.2 0.2
2 - Mobile Video and
Audio Communication
0.2 0.2 0.6
3 - Mobile Social Net-
working
0.2 0.6 0.2
where Str,ij,Epart od,ijk and Tpart od,ijk are the QoS parameters
related to the connection between the i-th access point and the
j-th SMD for partial offloading of the k-th application. The
weight parameters cqare associated to the importance of the
respective quality-related parameters in the performance of the
application.
The weight parameters cqare normalized with respect to a
certain application, so that it is possible to assume that:
3
X
h=1
ch= 1.
for each application k. Tab. III shows the considered weight
parameters for each type of application; it is worth to be
noticed that higher is a certain parameter ckfor a certain
application khigher is the importance of the related QoS
parameter for the selected application.
A. Cell Association
The above defined utility function is at the basis of the cell
association scheme that allows the selection by the SMD of
the best access point for respecting the requirements of the
considered applications; whenever a SMD requests to offload
an application, the utility function is evaluated for each access
point of the network. The SMD will connect to the access
point with the maximum utility function.
The selection of a certain access point for establishing the
connection could modify the values Str,Epart od and Tpart od
for the SMDs already connected with the same access point.
Hence, the utility function related to those SMDs is evaluated
again, by considering the new incoming SMD. The cell associ-
ation algorithm is reported in Algorithm 1, where it is possible
to note the utility function elaboration and the updating of
the utility function for all the SMD already connected to the
selected access point.
The Cell Association algorithm is performed for all the
SMDs in the scenario.
Algorithm 1 Cell Association Algorithm
Cell Association Algorithm
for all SMD do
Cell association request by the SMDj
for offloading the Appk
for all RATido
compute Str,ij
compute Epart od,ijk
compute Tpart od,ijk
compute Uij
associate SMDjwith RATas.t. Uajk = max(Uij)i
RATa.n =RATa.n + 1 // update the number of
SMDs associated to the RATa
for all SMDhassociated to RATado
compute Str,ah
compute Epart od,ahk
compute Tpart od,ahk
compute Uahk
end for
end for
end for
IV. NUM ER IC AL RE SU LTS
This section deals with the numerical results of the proposed
utility function approach by resorting to computer simulations.
The smart city scenario has been modeled in Matlab by con-
sidering a randomly placed number of SMDs in a deployment
area of 1000×1000 m2, where one LTE eNodeB with channel
capacity equal to 100 MHz and three WiFi access points with
channel capacities equal to 22 MHz are positioned to cover
the entire area. The SMDs, positioned randomly, will connect
to one of the access point/eNodeB, depending on the cell
association policy; to this aim we suppose that all the SMDs
are capable to connect to both WiFi and LTE. In Fig. 2 the
area in case of 500 SMDs is represented, where the access
points are positioned at point (0,0), (500,1000) and (0,1000),
and the LTE eNodeB at (500,500).
The SMDs, positioned randomly, request in sequence to
offload a random application type and are connected accord-
ingly, on the basis of the presented cell association algorithm.
The values Smd,Pid,Ptr and Plare specific parameters of the
mobile devices. We utilized the values of an HP iPAQ PDA
with a 400 MHz Intel XScale processor (Smd = 400) and the
following values: Pl0.9W,Pid 0.3Wand Ptr 1.3W.
As for the cloud server used for the offloading, we suppose
that Scs = 8000 [8].
As for the utility function, we have resorted to the following
values for the steepness of the sigmoid functions: α1= 1.6·
103,α2= 106, and α3= 106. These values have been
selected after a numerical optimization phase that we do not
report here due to space constraints. We have supposed that the
three applications are equally distributed among all the SMDs
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Fig. 2. Deployment area 500 SMDs, where the WiFi access points (circle
points) are positioned at point (0,0), (500,1000) and (0,1000), and the LTE
eNodeB (square point) at (500,500)
in the scenario, so that they have same probability equal to
1/3.
The numerical results are reported by focusing on the
performance in terms of average energy consumption, com-
putational time and throughput for each SMD. The numerical
results have been compared with three other approaches: local
computation, total offloading and nearest node. The local
computation algorithm considers that all the data processing
is performed locally by each SMD; in this case no data is
exchanged on the network. The total offloading considers the
opposite scenario where no data is computed locally, while it
is totally offloaded to centralized cloud servers. The nearest
node, instead, considers the case in which each SMD will
connect to the nearest AP/eNodeB.
In Fig. 3 the results in terms of energy consumption are
reported. It is possible to note that both the utility function and
the nearest node approaches outperform the local computing
and the total offloading. Moreover, it is possible to note
that the utility function algorithm allows to have almost the
same values for different numbers of nodes, outperforming
the nearest node approach for increasing number of SMDs.
A similar behavior can be noted in terms of average time for
executing the application, in Fig. 4, where, also in this case, the
utility function algorithm outperforms the other approaches.
In Fig. 5, the performance in terms of average throughput
has been reported. In this case the performance for the local
computation approach is not reported because in this case no
data transfer occurs. It is possible to note that the throughput
for the utility function algorithm remains stable, hence giving
an optimized value to each SMD, while in the nearest node
approach the throughput decreases when the number of SMDs
increases.
102103104
104
105
106Average Energy Consumption
SMD [n]
Ener gy Cons umption [ W ·s]
Utility Function Algorithm
Nearest Node Algorithm
Local Computation
Total Offloading
Fig. 3. Performance results in terms of average energy consumption with a
variable number of SMDs.
102103104
104
105
106Average Computation Time
SMD [n]
Comput atio n Time [s ]
Utility Function Algorithm
Nearest Node Algorithm
Local Computation
Total Offloading
Fig. 4. Performance results in terms of average computation time with a
variable number of SMDs.
102103104
103
104
105Throughput
SMD [n]
Throug hput [b/s]
Utility Function Algorithm
Nearest Node Algorithm
Total Offloading
Fig. 5. Performance results in terms of average throughput with a variable
number of SMD.
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V. CON CL US IO NS
The smart city environment is characterized by the presence
of several smart devices able to connect to different wireless
technologies; moreover, the most modern smart devices have
significant computing capability. One of the major challenges,
however, is to face with timing constraints imposed by the
applications and energy consumption of the devices. One of
the solution is to offload a portion of the application to a
centralized server. In this paper we have introduced an utility
function derived from the economic world aiming to optimize
the cell association of the smart devices for achieving low
energy consumption and computational time while maximiz-
ing the overall throughput. The proposed approach allows
to increase the performance with respect to a nearest node
association, and with respect to statical approaches where the
computation is performed locally or is completely offloaded.
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... In this section, the performance of the proposed mechanism is evaluated through numerical simulations designed by using the MATLAB. e compared algorithms are the competitionbased algorithm [30] and the user-satisfaction-based offloading algorithm [31]. eir features are described as follows: ...
... Different mobile devices have different CPU computing capacities. e HP iPAQ PDA with a 400 MHz Intel XScale processor [31] has the following parameters: the local processing power P l i � 0.9W, the standby power P id i � 0.3W, and the transmission power P tr i � 1.3W. In addition, the parameters of the other three mobile devices include CPU processing parameters, such as χ i , α i , and β i . ...
... It exhibits a relatively lower energy consumption when the number of mobile device users is small. However, with the explosive increase in the The proposed algorithm Competition-based algorithm [30] User-satisfaction algorithm [31] All tasks are executed locally Variance number of mobile device users, the performance degrades due to the traffic growth. Obviously, the proposed method can find a better energy-saving solution than other two approaches. ...
Article
Full-text available
Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel become critical issues. In this paper, the problem of the offloading decision is formulated as a 0-1 nonlinear integer programming problem under the constraints of channel interference threshold and the time deadline. Through the classification and priority determination for the mobile devices, a reverse auction-based offloading method is proposed to solve this optimization problem for energy efficiency improvement. The proposed algorithm not only achieves the task offloading decision but also gives the facility of resource allocation. In the energy efficiency performance aspects, simulation results show the superiority of the proposed scheme.
... Consequently, bandwidth has become a critical concern for computational offloading in the context of mobile Cloud computing [71]: The offloading effort is not preferred until the connection has sufficient bandwidth, and the benefit of offloading enlarges as the network bandwidth increases [42,72]. In particular, in addition to the TCP stream bandwidth between different computing resources [73,74], the researchers are also concerned with the bandwidth of network equipment (e.g., access points [75,69] and base station [41]). 3) Network Condition: Given the same communication coefficients, better channel quality improves Cloud applications' energy performance [34], while poor network conditions worsens both response time and energy efficiency [40,76]. ...
... 3) Network Condition: Given the same communication coefficients, better channel quality improves Cloud applications' energy performance [34], while poor network conditions worsens both response time and energy efficiency [40,76]. The network condition can be reflected by the signal strength or the signal to noise ratio [75]. When the signal strength is low, the relevant network devices will have to increase their power levels for data transmission [33], and will correspondingly end up with higher communication cost [43]. ...
... In general, maintaining high processing speed would consume more energy [34]. In mobile Cloud computing, the speeds of client devices and Cloud servers are usually discussed together, in order to calculate their computing speedup (i.e. the Cloud-client computing speed ratio) [75,71]. The bigger speedup might indicate the better offloading opportunity, and lead to the higher application performance and the lower energy consumption [42,41,70]. ...
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Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. Therefore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.
... However, these methods cannot be used in practice since they require excessive computation times. Furthermore, we compare our approach to a greedy algorithm [16] that implements a selfish behavior in allocating resources to users. Such three techniques -the proposed probabilistic algorithm, the greedy heuristic, and the exact method-are based on the definition of a proper objective function taking into account different requirements and characteristics of the considered scenario. ...
... Thus, our proposed approach allows to reach nearoptimal solutions in 'real time'. Moreover, we show that it outclasses a greedy heuristic solution, used as a benchmark, presented by the same authors in [16] and summarized in this paper for completeness. ...
... This paper addresses this problem by considering entire populations instead of each user individually, aiming at reducing overall 'community' or social costs. In contrast, in [16] the same authors proposed a utility function model derived from economics, in which the greedy heuristic summarized in Section IV-B was used for minimizing the collective cost for a community in mobile clouds. Our approach in this paper is focused on a global perspective, corresponding to a centralized vision that is more effective for a community. ...
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Smart cities represent rich and dynamic environments in which a multitude of smart mobile devices (SMDs) interact among them by sharing data. SMDs require from fast access to online services, but they offer limited computing capabilities and battery lifetime. SMDs make frequent use of computation offloading, delegating computing-intensive tasks to the cloud instead of performing them locally. In such a large-scale and dynamic environment, there might be thousands of SMDs simultaneously executing processes and, therefore, competing for the allotment of remote resources. This arises the need for a smart allocation of these resources. Accordingly, this paper proposes a biased-randomized algorithm to support efficient and fast link selection. This algorithm is able to provide “real-time” near-optimal solutions that outperform solutions obtained through existing greedy heuristics. Furthermore, it overcomes the responsiveness limitations of exact optimization methods.
... The purpose of these cost functions is to assign each parameter (i.e., the actual throughput and delay) a value in the [0, 1] range, which describes the fitness of the parameter for the user's needs. To take into account a target service value, we resort to a logistic function, whose application is often considered for modeling QoS user satisfaction in wireless communications [43]. For this objective, we have defined two logistic functions to map the throughput and the delay: ...
... To solve these instances, we employed an Intel Core i 9 -9980HK processor with 32 GB of RAM. Table 1 shows the parameter settings for the computational experiments [42,43,52]. Notice that the opening cost values for E i , which are derived from the number of facilities, are consistent with values considered in the literature for the energy spent for switching on an RSU [52]. ...
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Funding information The Uncapacitated Facility Location Problem (UFLP) is a popular NP-hard optimization problem that has been traditionally applied to logistics and supply networks, where decisions are difficult to reverse. However, over the years, many new application domains of the UFLP have emerged. Some of these applications require us to re-optimize the solution quickly, as inputs change slightly but frequently over time. For instance, the 5G communication standard considers several scenarios in which real-time optimization is needed, e.g., Internet of Vehicles (IoV), Virtual Network Functions placement , and Network Controller placement. Among these, IoV scenarios take into account the presence of multiple roadside units (RSUs) that should be frequently assigned to operating vehicles. In order to ensure the desired quality of service level, the allocation process needs to be carried out frequently and efficiently, as vehicles' demands change. In this dynamic environment, the mapping of vehicles to RSUs needs to be re-optimized periodically over time. This paper proposes an agile optimization algorithm designed to support fast re-optimization in the described dynamic environment. The algorithm is tested using a set of existing benchmark instances, and the experiments show that it can efficiently generate high-quality and real-time results in dynamic IoV scenarios.
... For this reason, a new urban framework, named urban MCC (UMCC), is developed herein. While in [4][5][6] specific solutions were introduced and analyzed, here the full system view is provided with requirements and an optimization framework. UMCC can be thought of as the technological nervous system, allowing the networks and information flows of the city to enjoy a better urban way of life. ...
... A Greedy Approach: If the offloading operation is advantageous with respect to the local computation, the cell association scheme allows the "best" node to be selected from the list of those available; such a list can be completed by each SMD that sorts each possible access node based on a self-calculated objective function [4]. If the offloading cost is lower than the cost of local computation, the SMD will connect to the node that minimizes the cost function; otherwise, it will locally compute the application. ...
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The increasing urbanization level of the world population has driven the development of a smart city geographic system, conceived as a fully connected wide area characterized by the presence of a multitude of smart devices, sensors, and processing nodes aimed at distributing intelligence into the city. At the same time, the pervasiveness of wireless technologies has led to the presence of heterogeneous networks, operating simultaneously in the same city area. One of the main challenges in this context is to provide sustainable solutions able to jointly optimize the data transfer, exploiting heterogeneous networks, and the data processing, exploiting heterogeneous devices, for managing smart city applications for citizens' communities. In this article, the UMCC framework is developed, introducing a mobile cloud computing model describing the flows of data and operations taking place in the smart city. In particular, we focus on the proposal of a unified offloading mechanism where communication and computing resources are jointly managed, allowing load balancing among the different entities in the environment, delegating both communication and computation tasks in order to satisfy the smart city application requirements. This allows us to cope with the limited battery power and computation capacity of smart mobile devices and plays a key role in a smart environment where wireless communication is of utmost relevance, particularly in the mobility and traffic control domains.
... In such a way, the fog infrastructure can be seen as an extension of the cloud infrastructure toward the edge in a hierarchical way for implementing a flexible approach. Similarly, the fog computing infrastructure is foreseen to be used not only for optimizing the data plane but also the control plane by introducing the concept of Fog-RAN (F-RAN) [20], where fog devices are able by themselves to manage the control plane of the RAN for respecting the users' requirements [21] (see Fig. 3). ...
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This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.
... The concept of offloading for IoT systems, has been limited to IoT mobile applications, in the context of smart-cities applications, which effectively is the same research related to mobile offloading as in the previous section. For example, Mazza et al. [15] propose a mechanism of offloading parts of smart-city applications to the best access point rather than the nearest one, based on a utility function which describes the Quality of Service in terms of energy cost, computational time and throughput. Rachuri [16] also proposes an offloading mechanism for mobiles based on a multi-criteria (energy, latency and data rate) decision theory, which classifies tasks and dynamically adapts to changes such as mobile battery status and users data plan allowance. ...
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