Cloud computing

2018 IEEE Wireless Communications and Networking Conference (WCNC)
Ultra-Low Latency Cloud-Fog Computing for
Industrial Internet of Things
Chenhua Shi∗ , Zhiyuan Ren∗ , Kun Yang†∗ , Chen Chen∗ , Hailin Zhang∗ , Yao Xiao∗ and Xiangwang Hou∗
∗ School
of Telecommunications Engineering, Xidian University, Xi’an, China
of Computer Sciences and Electrical Engineering, University of Essex, CO3 4HG, Colchester, UK
E-mail: {chshi@s-an.org, zyren@xidian.edu.cn, kunyang@essex.ac.uk, cc2000@mail.xidian.edu.cn,
hlzhang@xidian.edu.cn, yxiao@s-an.org, xwhou@s-an.org}
† School
Abstract—Recently, the industrial Internet of Things (IIoT)
has drawn high attention in academia and industry in the
context of industry 4.0. In the IIoT, smart IoT devices are
adopted to improve production efficiency. But, these devices will
generate huge amounts of production data, which need to be
processed effectively. To support IIoT services efficiently, cloud
computing is usually considered as one of the possible solutions.
However, the IIoT services still suffer from the high-latency
and unreliable links problem between cloud and IIoT terminals.
To combat these issues, fog computing is a promising solution
which extends computing and storage to the network edge. In
this paper, we are motivated to integrate the fog computing
to the cloud-based IIoT to build a cloud-fog integrated IIoT
(CF-IIoT) network. To achieve the ultra-low service response
latency, we introduce the distributed computing to the CF-IIoT
network and propose leveraging the real-coded genetic algorithm
for constrained optimization problem(RCGA-CO) algorithm to
optimize the load balancing problem of the distributed cloud-fog
network. Most importantly, considering the unreliable situation
in the CF-IIoT (e.g., fog nodes damage, wireless links outage),
we propose a task reallocation and retransmission mechanism
to reduce the average service latency of the CF-IIoT network
architecture. The performance evaluation results validate that
the RCGA-CO-based CF-IIoT and our proposed mechanism can
provide ultra-low latency service in IIoT scenario.
Index Terms—Industry 4.0, industry Internet of Things, cloudfog computing, ultra-low latency.
I. I NTRODUCTION
In the industrial Internet of Things (IIoT), massive intelligent devices generate explosive information requiring
processing. And in the context of industry 4.0, more and more
IIoT applications, such as smart manufacturing and industrial
automation, require real-time data processing. Therefore, a
powerful data center will be important in the IIoT.
In fact, cloud computing has been considered as the key
enabler for meeting the requirements of IIoT applications
[1]. However, the cloud-based IIoT network is still facing
some unsolved challenges. The cloud datacenters are always
deployed remotely, which leads to unbearable transmission
latency. Besides, the surging information generated by intelligent services makes the burden of the cloud server heavier,
and any fault in the network may results in large-scale
communication network error.
We notice that fog computing [2] is a promising solution
to address above challenges in the cloud-based IIoT. Fog
computing processes the workload locally on the fog nodes
near the terminals to reduce latency, which supports a new
breed of IoT applications and services that require low latency,
978-1-5386-1734-2/18/$31.00 ©2018 IEEE
mobility support and geo-distribution [2]. Therefore, we propose the cloud-fog based IIoT (CF-IIoT) network architecture
in the context of Industry 4.0, which leverages the current
edge devices (e.g. gateway, relay, or router) to build a fog
computing layer in the CF-IIoT architecture to meet the
requirements of the latency-sensitive IIoT applications.
Recently, there are many works about the fog computing
and improved cloud computing. In [3], the low latency and
low energy consumption performance of the fog computing is
validated by comparing with the traditional cloud computing;
In [4], authors focused on the service allocation problem
in the Combined Fog-Cloud architecture to minimize the
latency experienced; In [5], [6], authors proposed a novel
C-RAN architecture with the mobile cloud computing. And
in the IIoT area, authors in [7], [8] discuss the power
consumption and the real life application about IIoT. In [9],an
opportunistic spectrum access for Indoor Wireless Coverage
is researched. In [10], an effective management scheme for
heterogeneous sensor networks is proposed. However, these
researches didn’t consider how to joint multiple fog nodes
and the cloud server to perform tasks for further reducing
the service latency. In this paper, considering the relatively
weak computing and storage capacity of the fog nodes, we
propose to joint multiple fog nodes and the cloud server
to perform computationally intensive real-time tasks in a
distributed manner for further reducing the latency. In the
distributed computing, load balancing is a key technology to
reduce latency. Therefore, we investigate the load balancing
strategy of multiple fog nodes and the cloud server. And
proposing leveraging the real-coded genetic algorithm for
constrained optimization problem(RCGA-CO) load balancing
algorithm to make the optimal load balancing strategy.
Most importantly, the resource-poor fog node is easy to
damage. Besides, the unstable wireless transmission links may
lead to network faults. One single fog node cannot guarantee
to complete the workload on it. Therefore, we propose a
task reallocation and retransmission mechanism to reallocate
the uncompleted subtasks on the failure nodes according to
the RCGA-CO algorithm and retransmit the new subtasks
to normal nodes to guarantee the task could be completed
timely. The simulation results show that the reallocation and
retransmission mechanism could reduce the average service
latency in the case of fog nodes failure.
The reminder of this paper proceeds as follows. Section
II illustrates the CF-IIoT architecture. In section III, we
2018 IEEE Wireless Communications and Networking Conference (WCNC)
establish the latency mathematical model of the CF-IIoT, and
propose applying RCGA-CO algorithm to solve the problem.
The performance evaluations are shown in section IV. Finally,
we conclude this paper in section V.
II. CF-II OT A RCHITECTURE
To satisfy the applications’ requirements of low latency, we
introduce the fog computing into the cloud-based architecture
to build CF-IIoT architecture, which is shown in Fig. 1.
Broadband communication
Wifi
3G/4G
Cloud
Internet
Gateway
Fog
III. T HEORETICAL M ODEL AND THE RCGA-CO- BASED
L OAD BALANCING S TRATEGY IN CF-II OT
In this section, we establish the theoretical latency model
in the CF-IIoT and propose leveraging RCGA-CO algorithm
to solve the problem.
A. The Latency Mathematical Model in CF-IIoT
Taking the real-time path planning of the industrial robot
with laser navigation as an example, there exists a multitude
of data processing during the path planning [11]. We analyze
the data processing in the CF-IIoT architecture. Firstly, the
robot collects the path data information in real time. And
then the collected real-time path data is transferred to the local
fog devices and cloud to perform fast distributed computing.
Finally, the path planning results will be sent to the robot to
conduct the behavior of the robot. Refer to the above task,
the network architecture illustrated in Fig. 1 is abstract as a
weighted undirected graph G = (V, E) as shown in Fig. 2.
C
Wireless sensor
nodes
Manufacturing
equipments
Users and smart
terminals
Wv3 ,vc
Wv3 ,vk
v3
Wv2 ,v3
Fig. 1. The CF-IIoT architecture.
The architecture is divided into three layers: the cloud
service layer, fog computing layer and infrastructure layer.
The infrastructure layer is the basic component of the
architecture, which is mainly composed of sensor nodes, manufacturing equipment, conveyor systems, intelligent industrial
robots, manipulators, smart terminals and so on. The function
of the infrastructure is performing specific production activities, such as collecting data, manufacturing, and logistics.
The fog computing layer consists of multiple edge network
devices (e.g., gateways, routers, switches) with relatively poor
computing and storage capacity, which play an important role
in the CF-IIoT architecture. In this architecture, we choose
the gateways as the fog nodes. In order to facilitate the
deployment and expansion of the fog network, fog nodes
communicate with each other via the wireless channels.
The fog nodes interact with the cloud to obtain the related
information and service according to the requirements of
the infrastructure layer, store the pivotal product information
transferred through them, and upload the valuable production
status information to the cloud to achieve the global data
sharing. Most importantly, all kinds of the data (e.g., product
data, users’ requirement data and measurement data) could be
processed in the fog computing layer to reduce the latency.
And we propose executing distributed computing in the
cluster composed of multiple fog nodes and cloud servers and
balance the load of each fog node and cloud to achieve the
ultra-low latency. In addition, due to the unreliable situation
of the fog node and cloud, the distributed computing paradigm
adopting task reallocation and retransmission mechanism in
the cloud-fog network is a good method to further improve
the real time performance of the architecture.
The cloud service layer consists of high performance cloud
servers, which is responsible for storing IIoT data,sharing the
global information and simple data mining operations. In our
architecture, the cloud is also seen as a computing node to
improve the computing capacity of the architecture.
vk
v2
Wv1,vk
v1
Wv1 ,v2
Fig. 2. The weighted undirected graph.
In Fig. 2, the vertex set V = {v1 , v2 , · · · , vk , C}, where
vertex vi and C represent the fog nodes and cloud server respectively. In order to enhance the computing capability of the CF-IIoT architecture, we introduce the cloud
server as a computing node, and the computing capability is denoted by Cc . Meanwhile, the computing capacity
of each fog node is represented by Cvi . The edge set
E = {ev1 ,v2 , · · · , evi ,vj , · · · , evk−1 ,vk , ev3 ,c }, where each
edge evi ,vj represents a wireless communication link between
nodes vi and vj . And the weight of each edge, i.e., Wvi ,vj
denotes the communication latency between vi and vj .
In the CF-IIoT network, industrial terminals send their
ultra-low latency service requests to the nearest fog node,
i.e., vj , which is considered as the master node firstly. Then,
these terminals transfer their application task i.e., D, to the
master node. Next, the computing task could be divided into
several small subtasks, i.e., Di . These subtasks are performed
by all the fog nodes including master fog node vj and slave
fog nodes, i.e, vi , and cloud server C. Finally, the computing
results would be converged by the master node and sent back
to the industrial terminals. Thus, the service latency t in CFIIoT can be expressed as:
{
}
Di
Dc
t = max
+ Wvi ,vj lvi ,vj ,
+ Wvj ,c lvj ,c
Cvi
Cc
i, j = 1, 2, · · · , k
(1)
where CDvi is the computation latency of the subtask Di on
i
the fog node vi , Wvi ,vj lvi ,vj is the communication latency
between fog nodes vi and vj . And lvi ,vj denotes whether
there exists a subtask allocation relationship between the two
fog nodes vi and vj . lvi ,vj = 1 means the relationship exists;
2018 IEEE Wireless Communications and Networking Conference (WCNC)
lvi ,vj = 0 denotes the relationship doesn’t exist. Similarly,
Dc
Cc is the computation latency of the subtask Dc on the
cloud server C, and Wvj ,c lvj ,c represents the communication
latency between master fog node vj and the cloud C.
Moreover, referring to [12], the wireless communication
latency between the nodes in the CF-IIoT, i.e., Wvi ,vj , Wvj ,c ,
under the stop-and-wait ARQ protocol could be described as:
1 + P ei
Di
×
(2)
ri
1 − P ei
Dc
1 + P ec
Wvj ,c =
×
(3)
rc
1 − P ec
Where Di and Dc are the subtasks that are transmitted to
the node vi and the cloud server C. ri and rc denote the
data transfer rate of the link evi ,vj and evj ,c . P ei and P ec
represent the packet error rate of the link evi ,vj and evj ,c .
To obtain the ultra-low latency, we must find an optimal
task allocation strategy, namely find a group of optimal
subtasks {D1 , D2 , · · · , Di , · · · , Dk , Dc }. In summary, the
load balancing problem in the CF-IIoT can be formulated
as the following optimization problem:
{
}
Di Di (1 + P ei )
Dc Dc (1 + P ec )
min max
+
lvi ,vj ,
+
lvj ,c
Cvi ri (1 − P ei )
Cc rc (1 − P ec )
i, j = 1, 2, · · · , k (4)
Wvi ,vj =
{
s.t. lvi ,vj =
1, Di ̸= 0
0, Di = 0,
0 ≤ D i , Dc ≤ D
k
∑
Di + Dc = D
{
lvj ,c =
1, Dc ̸= 0
0, Dc = 0
(5)
i=1
B. The Average Service Latency Mathematical Model in the
Case of Fog Nodes Failure in IIoT
Considering the unreliable situation in the CF-IIoT, e.g.,
fog nodes damage or wireless links outage. In this paper, we
are collectively called the fog node damages probability and
the wireless link outage probability as the failure probability
of one single fog node. Assuming the failure probability of
each fog node is pi . When some fog nodes fail, the task
can’t be completed if we do not adopt any mechanism. In this
paper, when some fog nodes fail, we propose reallocating the
subtasks on the failure fog nodes and retransmitting the corresponding subtasks to the normal fog nodes and cloud server to
perform distributed computing for reducing the latency. The
reallocation and retransmission mechanism could guarantee
that the tasks could be completed timely and correctly. Based
on the proposed mechanism, the mathematical model of the
average service latency in the case of the fog nodes failure in
CF-IIoT could be expressed as Eq. (6).
Where V = {v1 , v2 , · · · , vk } is the collection of fog nodes,
V ′ is the collection of normal fog nodes, and V − V ′ is
the collection of failure fog nodes. pn or pi is the failure
probability of the fog node. We set the subtask timeout period
to be tout . When the master node can’t obtain the subtask
processing results from the corresponding slave fog nodes
within tout time period, the system think that the slave fog
nodes fails, which means vn ∈ V − V ′ . Then, the master
fog node, i.e., vj , reallocates the subtasks on the failure fog
nodes, and the result of the reallocation is Di′ , Dc′ . Next,
the master fog node retransmits the corresponding subtasks
Di′ , Dc′ to the normal fog nodes and cloud server to execute
distributed computing. Finally, the subtask processing results
are transferred to the master node to converge and the final
result is returned to the industrial terminals timely.
In Eq. (6), Di , Dc , Di′ , Dc′ , lv′ i ,vj and lv′ j ,c meet the
following constraints:
0 ≤ Di , Dc , Di′ , Dc′ ≤ D
∑
vi ∈V
′
{
lv′ i ,vj
=
∑
Di + Dc +
vi ∈V
Di′ + Dc′ = D
0, Di′ = 0,
(8)
′
{
1, Di′ ̸= 0
(7)
lv′ j ,c
=
1, Dc′ ̸= 0
0, Dc′ = 0
(9)
C. GA-based Load Balancing Algorithm
To resolve the load balancing problem in Eq. (4) and optimize the latency in Eq. (6), we introduces a real-coded genetic
algorithm [13] for constrained optimization problem(RCGACO). In the process of the real-coded GA, each individual
Xi = {xi1 , xi2 , · · · , xi(k+1) } in the population denotes a
possible solution of the optimization problem, which would be
initialized with real number randomly. And then, an optimal
individual is found through the constant evolution of selecting,
crossing over and mutating the initial population. In order
to solve the constrained optimization problems including
inequality and equality constraints effectively, we levarage
the RCGA-CO to solve the optimization problems with
constraints, which transform the constrained optimization
problems into the unconstrained optimization problems. The
following gives a description of the RCGA-CO.
Different from the traditional real-coded GA, the fitness
function in the RCGA-CO calculated as follows [14]:

t(X)
X∈F



f (X) =
k+2
∑


tj (X) + ξ(X, g) X ∈ S − F
 t(X) + h
j=1
(10)
Where F is the feasible region in the search space S, and
S − F represents the infeasible region. h denotes the penalty
factor, tj (X) is the constraint violation value of the infeasible
individuals for the jth constraint, and ξ(X, g) indicates an
additional heuristic value for infeasible individuals in the gth
generation. tj (X) and ξ(X, g) could be expressed as follows:

max(0, −X(j))
1≤j ≤k+1


tj (X) =
(11)
k+1
∑

 |
X(i) − D |
j =k+2
i=1
ξ(X, g) = W orst(g) −
min
X∈S−F
{
}
k+2
∑
t(X) + h
tj (X)
j=1
{
}
W orst(g) = max W orst(g − 1), max {t(X)}
X∈F
(12)
(13)
2018 IEEE Wireless Communications and Networking Conference (WCNC)
ta =
∑
∏
vn ∈V −V ′
pn
(
{
}
Di (1 + P ei )
Dc Dc (1 + P ec )
Di
+
lvi ,vj ,
+
lvj ,c , tout
(1 − pi ) min max ′
vi ,vj ∈V
Cvi
ri (1 − P ei )
Cc rc (1 − P ec )
vi ∈V ′
{ ′
})
Di
D′ (1 + P ei ) ′
D′ D′ (1 + P ec ) ′
+ min max ′
+ i
lvi ,vj , c + c
lvj ,c
vi ,vj ∈V
Cvi
ri (1 − P ei )
Cc rc (1 − P ec )
∏
In Eq. (13), t(X) represents the fitness value of the gth
generation feasible individuals. W orst(g) records the feasible
individual with the best fitness through g generation evolution.
In the RCGA-CO, each chromosome, namely each individual Xi in the population is designed as a one-dimensional real
array with k + 1 genes, which should be randomly initialized
with real number in the searching space S firstly. Then, the
fitness value of each individual would be calculated according
to Eq. (10) to evaluate the population. Next, the genetic
operators are performed to update the initial population. And
the specific genetic operators are given as follows:
Selection: Selection is the process of preserving high fitness
individuals from the current population. In this paper, 2tournament selection strategy is adopted.
Crossover: Crossover is an important method of genetic
algorithm to passed the original good genes onto the offspring.
The arithmetic crossover is used in the RCGA-CO, in which
two new children individuals , i.e., X1′ ,X2′ are generated by
a linear combination of the two parent individuals, i.e., X1 ,
X2 . The relationship between offspring and parents could be
described as follows:
X1′ = λX1 + (1 − λ)X2
(14)
X2′ = λX2 + (1 − λ)X1
(15)
Where λ is a random number on interval (0, 1).
Mutation: Mutation operation determines the local search
capability of the RCGA-CO and improves the diversity of
individuals in the population. In our paper, the non-uniform
mutation operator is applied.
Taking the load balancing problem in Eq. (4) as an example,
the basic steps of RCGA-CO are shown in Algorithm 1.
IV. P ERFORMANCE EVALUATION
In this section, we present performance evaluations about
the RCGA-CO based CF-IIoT and our proposed retransmission and redistribution mechanism. Referring to [15], the
number of fog nodes in the CF-IIoT is set to 4 to achieve low
latency. The Wireless transmission parameter settings used in
the simulation were based on 802.11ac protocol. For simulating the real network environment,the computing capacity
of each fog device is different to construct the heterogeneous
fog network. Some related parameters are given in TABLE I.
The parameter settings of the RCGA-CO are as follows:
The population size is 100, the maximum number of generations is 200, the crossover and mutation probability are 0.9
and 0.05 respectively, and the value of W orst(0) is 106 .
In our simulation, the task loads are the total service request
tasks data from the smart industrial equipment. These task
loads are simulation settings, which vary from 0Mb to 500Mb.
To reduce the service latency, we can get the task allocation
(6)
Algorithm 1 RCGA-CO algorithm
Input: D,Cvi ,Cc ,ri ,P ei ,P ec ,W orst(0)
M axG: max iterations
pc: Crossover probability; pm: Mutation probability
Output: t(X): Best solution
1: Randomly initialize each individual Xi in the Population
2: for Generation:1 to M axG do
3:
for each individual Xi ∈ Population do
4:
Calculate the value of tj (X) using equation (11)
5:
end for
6:
Calculate the value of ξ(X, g) and W orst(g) using
equation (12) and (13)
7:
for each individual Xi ∈ Population do
8:
Calculate fitness value f (Xi ) using equation (10)
9:
if f (Xi )>localBestFitness then
10:
localBestFitness = f (Xi )
11:
store Xi
12:
end if
13:
end for
14:
if localBestFitness>globalBestFitness then
15:
globalBestFitness =localBestFitness
16:
store the corresponding Xi whose fitness is the
globalBestFitness
17:
end if
18:
Select individuals from the Population;
19:
if rand < pc then
20:
crosspop = cossover(Population,pc)
21:
end if
22:
if rand < pm then
23:
mutatepop = mutate(crosspop,pm)
24:
end if
25:
Update the Population: Population = mutatepop
26: end for
27: Calculate the optimal t(X) using equation (4) and (5)
TABLE I
T HE RELATED PARAMETERS OF CF-II OT
Parameter type
v1
v2
v3
v4
C
Cvi /Cc (Gbps)
Transfer rate(Mbps)
Packet error rate
0.3
200
0.0132
0.25
200
0.0241
0.2
150
0.0204
0.15
250
0.0173
10
20
0.0091
ratio according to the RCGA-CO algorithm in advance. When
the task arrives, the task allocation results could be obtained
quickly according to the task allocation ratio. Therefore, the
program running time for RCGA-CO could be ignored. All
the simulation results are derived by MATLAB and the values
are the means of many repeated experiments.
2018 IEEE Wireless Communications and Networking Conference (WCNC)
A. Latency Performance Comparison among the Three Architectures in IIoT
In the simulation, we compare the service latency of the
CF-IIoT based on RCGA-CO load balancing algorithm with
the cloud-based architecture and fog-based architecture in
IIoT to validate the low latency performance of CF-IIoT.
12
10
Cloud
Fog
CF−IIOT
C. The Average Service Latency Performance in the Case of
Fog Nodes Failure
Latency/s
8
6
4
2
0
0
100
200
300
Task/Mb
400
500
Fig. 3. Latency performance comparison among the three architectures.
Fig. 3 shows the latency comparison results. With the
increasing of the task, the latency in the cloud-based architecture is significantly higher than that in CF-IIoT and fogbased architecture due to the increasing transmission latency.
Compared with the cloud-based architecture, the reason why
fog-based architecture has lower latency is that the cloud is
far from the smart industrial terminals and there is limited
bandwidth. Furthermore, in Fig. 3, it’s seen that the service
latency of CF-IIoT is lower than the fog-based architecture
when the workload is large. This is because the computing
capacity of the cloud server enhance the computation power
of the CF-IIoT. When the task load is 500Mb,we can observe
that the latency performance of CF-IIoT improved by 92.9%
and 35.7% compared with the cloud-based architecture and
fog-based architecture respectively. Therefore, the CF-IIoT is
very suitable for IIoT scenario to provide low latency service.
B. Latency Performance Comparison among Multiple Load
Balancing algorithms
In this section,we analyze the high efficiency of the RCGACO load balancing algorithm in reducing latency in CF-IIoT
by comparing it with the PSO-CO [16], Weighted Round
Robin (WRR) [17] and Greedy load balancing algorithm
(GreedyLB) [18]. The simulation result is shown in Fig. 4.
2.5
Latency/s
2
WRR
GreedyLB
PSO−CO
RCGA−CO
1.5
1
0.5
0
0
the service latency. Moreover, the PSO-CO may fall into
the local optimum, the WRR and GreedyLB algorithm don’t
consider the transmission latency when balancing the load,
which would lead to higher latency. When the task quantity
is 500Mb, the latency performance of RCGA-CO algorithm
improved by 69.2%, 63.9%, 38.9% compared with WRR,
GreedyLB and PSO-CO, respectively. Hence, the RCGACO load balancing algorithm is observed to be an efficient
solution in CF-IIoT architecture to reduce the service latency.
100
200
300
Task/Mb
400
500
Fig. 4. Latency performance comparison among multiple load balancing
algorithms.
As the task quantity increases, we notice that the RCGACO algorithm obtained lower latency than other three algorithms. In RCGA-CO, the crossover operation guarantees the
global searching ability and the mutation operation determines
the local search capability. Therefore, the RCGA-CO algorithm can achieve a better global load balancing strategy to reduce
In the simulation, we assume each fog node has a failure
probability. To evaluate the performance of the CF-IIoT
adopting task reallocation and retransmitting mechanism, we
compare the average service latency in three cases: normal
case where all the fog nodes are normal, a failure case where
the CF-IIoT adopting task reallocation and retransmitting
mechanism, and another failure case where the CF-IIoT only
adopting retransmitting mechanism. Moreover, to investigate
the influence of the fog nodes failure probabilities on the service latency in CF-IIoT, we compare the latency in four kinds
of production environments where the failure probabilities of
the fogs nodes are different. The failure probabilities of the
fog nodes in four environments are given in TABLE II. The
simulation results are shown in Fig. 5 and Fig. 6.
TABLE II
T HE FAILURE PROBABILITIES OF THE FOG
NODES IN FOUR
ENVIRONMENTS
Environment
v1
v2
v3
v4
C
Best Environment
Good Environment
General Environment
Poor Environment
0
0.001
0.31
0.6
0
0.1
0.36
0.63
0
0.01
0.4
0.65
0
0.016
0.33
0.69
0
0
0
0
In Fig. 5, we compare the average service latency in
three cases: all the nodes in the CF-IIoT are normally running(Normal.Latency), some fog nodes fail in the CF-IIoT
adopting task reallocation and retransmitting mechanism(FReaRet.Latency) and some fog nodes fail in the CF-IIoT
only adopting retransmitting mechanism (F-Ret.latency) that
the uncompleted subtasks on the failure fog nodes only
retransmitted to one normal node to compute. The failure
probabilities of the fog nodes in the failure case are set
according to Good Environment in TABLE II. From Fig. 5,
we can firstly observe that the task could be completed in
a certain latency instead of can not being completed when
fog nodes fail. With the increase of the task quantity, the
latency in failure case (F-ReaRet.Latency and F-Ret.Latency)
is higher than the latency in normal case(Normal.Latency).
This is because the uncompleted tasks on the failure nodes
need to be reprocessed on the normal nodes, which will lead
to the increasing of the latency. Besides, we can see that
the F-ReaRet.Latency is lower than the F-ret.latency and the
difference of the latency between them is increasing gradually
as the task increases. Because reallocating the uncompleted
task on the failure fog nodes to all the normal nodes to
perform distributed computing could efficiently reduce the
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ACKNOWLEDGMENT
1
Latency/s
0.8
F−Ret.Latency
F−ReaRet.Latency
Normal.Latency
0.6
0.4
0.2
0
0
100
200
300
Task/Mb
400
500
Fig. 5. The average service latency comparison in three cases.
1.4
1.2
Latency/s
1
Poor ENV
General ENV
Good ENV
Best ENV
R EFERENCES
0.8
0.6
0.4
0.2
0
0
100
200
300
Task/Mb
This work was supported in part by the National Key Research and Development Program of China(2016YFE0123000),the National Natural Science Foundation of China (61201133, 61571338,61671347,61572389,
61620106011), the National Key Research and Development
Program of China(SQ2016YFHZ021501), the key research
and development plan of Shaanxi province(2017ZDCXL-GY05-01), UK EPSRC Project NIRVANA (EP/L026031/1), EU
H2020 Project iCIRRUS (GA-644526), 111 Project in Xidian
University of China (B08038).
400
500
Fig. 6. The average service latency comparison in four environments.
processing latency of the uncompleted task, especially when
the task is large.When the task load is 500Mb,we can observe that the F-ReaRet.Latency reduced by 8.1% compared
with the F-Ret.latency. Therefore, the CF-IIoT adopting task
reallocation and retransmitting mechanism could provide low
latency service in the case of fog nodes failure in IIoT.
Fig. 6 shows the influence of the fog nodes failure probabilities on the average service latency in CF-IIoT. We compare
the average service latency among four kinds of different
failure probabilities production environments. The failure
probabilities of the fog nodes in four environments are shown
in TABLE II. It’s obvious that the service latency increases
with the growing number of the task loads in four kinds of
environments. Moreover, we can observe that when the task
load is fixed, the average service latency shows an upward
trend with the increase of the failure probability. When the
task load is 500Mb, the latency in the best environment
reduced by 31.8%, 23.3% and 12.1% compared with the
latency in the poor environment, general environment and
good environment respectively, which means the large failure
probability of the fog nodes would lead to large average
service latency in the CF-IIoT.
V. C ONCLUSION
In this paper, we integrate fog computing to the cloudbased IIoT architecture to build CF-IIoT network architecture
and introduce distributed computing to the network. We first
establish the latency mathematical model of the CF-IIoT.
Then, we introduced RCGA-CO algorithm to balance the load
for reducing latency. Furthermore, considering the unreliable
situation in the CF-IIoT, we proposed a task reallocation
and retransmission mechanism to reduce the average service
latency in the case of fog nodes failure. The simulation
results demonstrate that the RCGA-CO-based CF-IIoT and
our proposed task reallocation and retransmission mechanism
could provide ultra-low latency in the IIoT. In future research
work, we mainly focus on the modeling of the reliability and
improving the reliability of the cloud-fog network.
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