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OPTIMIZATION
A novel hybrid arithmetic optimization algorithm and salp swarm
algorithm for data placement in cloud computing
Ahmed Awad Mohamed
1
•Ashraf D. Abdellatif
2
•Alhanouf Alburaikan
3
•Hamiden Abd El-Wahed Khalifa
3,4
•
Mohamed Abd Elaziz
5,6
•Laith Abualigah
7,8,9,10,11,12
•Ahmed M. AbdelMouty
13,14
Accepted: 28 December 2022
The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
Abstract
In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial
and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology
applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing
reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to
sensors in different geographic locations. There are three critical issues:—what data should be replicated?—when should
the data be replicated? and—where the new replicas should be placed? These three main open questions must be tackled
for data replication in the cloud environments. This strategy, the identification and mode of the data replication problem are
designed as a multi-objective optimization with modern meta-heuristic optimization method. Therefore, a new hybrid
method using Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA) is proposed in this paper.
Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm
algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing.
Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic
locations. The performance of the designed AOASSA strategy to tackling the data replication problem is evaluated using
different datasets of different sizes. To validate the AOASSA strategy, a set of experiments was carried out to validate the
proposed strategy AOASSA. Experiment results show the superiority of AOASSA over its competitors in terms of
performance measures, such as least cost path, distance, and bandwidth.
Keywords SBER Data replication SSA AOA Fog computing IoT Multi-objective optimization
1 Introduction
With the advancement of Internet of Things applications in
all fields, the big data volumes increased, and the use of
many applications in cloud computing environments
(Nguyen et al. 2019; Ghasempour 2019; Fu et al. 2018). At
the same time, with the increase in the amount of data and
information from users via cloud computing, sensors
increase exponentially, and data is stored in different
geographic locations on fog computing (Lin et al. 2016;
Liu et al. 2016; Abualigah and Diabat 2020). Cloud com-
puting is one of the best solutions for placing and recalling
data in different geographic locations and reducing user
waiting time. Cloud computing has developed its resources
to enable users to access various services such as servers,
storage, sensors, etc. (Wang et al. 2018; Yang et al. 2020).
With the development of cloud computing with the Internet
of things, huge amounts of different data are generated,
which require processing and storage across different
geographical locations. Cloud computing depends on load
balance, bandwidth, and network performance in waiting
time, the requirements of users based on the Internet of
Things. Response time for users due to delays across nodes
in cloud computing requires less bandwidth (John and
Mirnalinee 2020; Pallewatta et al. 2022; Wang and Zhang
2020; Taghizadeh et al. 2021; Torabi et al. 2022; Jin et al.
2022).
Fog computing extends cloud services to the IoT net-
work’s edge, offering numerous benefits such as high
performance, reduced response time, bandwidth, and load
Extended author information available on the last page of the article
123
Soft Computing
https://doi.org/10.1007/s00500-022-07805-2(0123456789().,-volV)(0123456789().,-volV)
balancing in fog computing (Liu et al. 2022; Li et al.
2022a; Yousif et al. 2022). Fog computing supports the
Internet of Things and provides sensors in cloud environ-
ments. It also provides users with services from virtual
machines and scalable storage spaces (Peake et al. 2022;
Haris and Zubair 2021; Khemili et al. 2022). Cloud com-
puting infrastructure is one model that manages cloud
resources efficiently and effectively. It enables users to use
the available resources as per usage and pay for usage.
Cloud computing is widely used for big data processing
and complex problems in different environments(Mahesh-
wari et al. 2012; Long et al. 2014; Boru et al. 2015). Cloud
computing that supports the Internet of Things offers a
promising way to identify and establish replication across
nodes in different geographic locations. Data replication is
one of the most widely used methods for data availability,
reliability, and distribution across nodes. It also works to
process data and files and access file replication at the
lowest cost and lowest path (Ebadi and Navimipour 2018;
Chuang and Hsiang 2022). Data replication reduces data
transfer, accessing data and files with less time and less
distance, and reducing user waiting time (Failed 2013).
Replication techniques consist of static and dynamic
methods of defining, establishing, and placing replication
across nodes. Static replication is set and established during
system configuration and is not affected by momentary
changes such as changing, deleting, or storing data within
the database. At the same time, dynamic replication pat-
terns are the access, storage, and deletion of data and its
availability (Grami 2022). There are three critical issues:
(1) what data should be replicated? (2) when should the
data be replicated? and (3) where the new replicas should
be placed? These three main open questions must be
tackled for data replication in the cloud environments
(Salem et al. 2020; Awad et al. 2021a,b).
The major contributions of the article are as follows:
•Improving a new hybrid swarm intelligent algorithm
method AOASSA is the strategy for dynamic data
replication of IoT applications tasks in fog computing.
The integration of AOA and SSA with MOO to solve
the least cost path in fog computing.
•To the best of our knowledge, there are introducing a
few papers that handle data transmission and the least
cost path for optimal placement file replication with
MOO.
•To the best of our knowledge, our work is the first
attempt to study a new hybrid AOA, SSA (AOASSA),
solved to obtain low bandwidth, data transmission, and
least cost path.
•The application of global optimization utilizing
AOASSA gives better results when experimental results
are compared with MOE, and MORM.
•AOASSA minimizes the computational complexity and
it also works efficiently for transmission data, distance
and placement data replication problems.
•The experimental results show the superiority of the
AOASSA algorithm performance over other algo-
rithms, such as cost, time, bandwidth, data transmis-
sion, and least cost path.
The rest of this paper is organized as follows. Section 2
presents related work. Section 3presents the proposed
architecture. Section 4presents the proposed strategy.
Section 5presents the experimental results—finally,
Sect. 6 presents the conclusion and future work.
2 Related work
Many related studies have researched data replication
strategies in the cloud, as follows:
•Sarwar et al. (2022) proposed two cross-node replica-
tion privacy schemes for data protection, authentica-
tion, and reliability. Create a schema for cross-node
select and placement data replication while maintaining
privacy and confidentiality on fog computing. The
proposed algorithm outperformed other algorithms
regarding memory, cost, confidentiality, and privacy.
•Chen et al. (2021) proposed the first decentralized
system to demonstrate data retrieval and repeatability—
BOSSA, which is compatible with all parties on
blockchain platforms. BOSSA also incorporates pri-
vacy-enhancing technologies to prevent decentralized
peers (including blockchain nodes) from inferring
private information about external data. The security
analysis is presented in the context of integrity, privacy,
and reliability, and we implement a BOSSA-based
prototype to leverage smart nodes on the Ethereum
blockchain. Our extensive beta reviews demonstrate the
practicality of our proposal.
•Li et al. (2022b) proposed Suggested a Lagrange
method for relaxation. This method considers the load
balancing, storage, data dependency, data transmission,
time, cost, and bandwidth to obtain minimum data
transmission time between nodes. Aimed at the cloudlet
and fault-tolerant task scheduling strategy is proposed.
The strategy optimization of the task scheduling
techniques by considering the task of time, cost, and
energy method. The experiments proved the perfor-
mance of the proposed strategy in transferring data
through cloud computing and choosing the optimal
location according to the proposed algorithm.
•Shiet et al. (2022) developed a new approach named
Multi-Cloud Application deployment (MCApp).
MCApp merges iterative mixed integer linear
A. A. Mohamed et al.
123
programming with domain-tailored large node search to
optimize data replication deployment and user requests.
The experiments proved the performance of the
proposed strategy using the real and datasets demon-
strate that MCApp significantly outperforms other
algorithms.
•Majed et al. (2022) presented a hybrid strategy for peer-
to-peer data replication in cloud computing environ-
ments. It chose the most suitable and optimal nodes in
the network at a low cost. It also selects users’ most
common and accessible data files and puts them in the
most appropriate placement. Experimental results
showed improved network performance and reduced
user waiting.
•Li et al. (2022c) Suggested an algorithm based on the
Lagrangian relaxation method for optimum data repli-
cation across nodes in cloud computing. Consider
balancing loads, bandwidth, and transmission time.
Also, use the Floyd algorithm to reduce cost and
bandwidth. The results showed the superiority of the
proposed algorithm over other algorithms.
•Khelifa et al. (2022) Suggested a dynamic and periodic
data replication strategy in cloud computing. The
proposed strategy aims to reduce the time for users’
requirements, achieve load balance, and reduce waiting
for time and speed access to data. It also reduces the
time to send data and transfer it through cloud
computing. Also, a fuzzy logic algorithm of select
and placement data replication across nodes was used.
The proposed algorithm proved to be superior to other
algorithms.
•Mohammadi et al. (2022) Suggested an algorithm for
selecting and setting data replication across nodes in
cloud computing. Use the hybrid fuzzy logic and ant
colony optimization algorithm to reduce users’ waiting
time and discover the most suitable and optimal nodes
for placement data replication. The proposed algorithm
outperformed the other algorithms.
3 Suggested system and discussion
3.1 Proposed system and structure
This section describes the proposed selection and place-
ment of data replication across nodes in fog computing.
The critical component of the proposed strategy is the Fog
Broker, which is set in the fog nodes layer. Fog Broker
consists of three stages: Task Manager, Resource Moni-
toring Service, and Task Scheduler. The fog computing
system relies on our dynamic data replication strategy
based on IoT in cloud computing. Select and placement
data replication cross node in cloud computing requires a
set of configurations to move data across fog computing.
We assume that our proposed strategy comprises a certain
number of Fog nodes such as (DCs) data centers and IoT
services. We organized the proposed strategy from differ-
ent geographic locations to select and place data replication
across nodes in fog computing. Services can be distributed
on any DCs, Fog nodes, or IoT sensors. Use the AOA
algorithm with the SSA algorithm to transfer data across
nodes with the lowest path and lowest cost. MOO with a
floyed algorithm was also used to reduce bandwidth across
the network and balance in fog nodes. The system in Fig. 1
consists of a different set of geographically distributed
node (G) and the structure is composed of many different
locations.
3.2 Arithmetic optimization algorithm (AOA)
3.2.1 Initialization phase
According to Matrix, AOA, the optimization criteria apply
to several produced random solutions (X). The best result
obtained in each iteration is saved as being close to the
current optimal result (Abualigah et al. 2021; Abdol-
lahzadeh et al. 2021; Abdollahzadeh and Gharehchopogh
2022; Mahajan and Pandit 2021; Mahajan et al. 2022a,b).
Xi¼
x1
1x1
1 x1
1;D
x2
1x2
2... x2
D
.
.
...
...
..
.
.
xN
1xN
D... xN
D
2
6
6
6
43
7
7
7
5ð1Þ
Based on the Math Optimizer Accelerated (MOA)
function produced by utilizing Eq. (2), the search phase
(exploration or exploitation) should be chosen for each
iteration.
MOA C IterðÞ¼Min þC Iter Max Min
MIter
ð2Þ
G1
G2
G3
G4
G5
G6
G7
Fig. 1 Proposed strategy for data replication in fog computing
A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in...
123
where MOA(C_Iter) is the value of MOA at the current
iteration. C_Iter is the current iteration, which is between
[1 M_Iter]. M_Iter is the maximum number of iterations.
Min and Max are the minimum and maximum of the
accelerated function values.
3.2.2 Exploration phase
The simplest rule that can simulation the behaviour of
mathematical operators was employed. The following
location updating calculations for the exploratory part:
xi;jC Iter þ1ðÞ
¼best xj
MOP þeðÞUBjLBj
lþLBj
;r2\0:5
best xj
MOP UBjLBj
lþLBj
;otherwise
(
ð3Þ
where x
i
(C_Iter ?1) indicates the ith solution location in
the next repetition. x
i,j
(C_Iter) indicates the jth location of
the ith solution. best(x
j
) is the jth in the best-obtained
location in the ith solution. lis an adjusting parameter used
to select the search operation, which is set equal to 0.5.
MOP C IterðÞ¼1C Iter 1
a
ðÞ
MIter 1
a
ðÞ ð4Þ
where MOP is a coefficient. MOP(C_Iter) indicates the
value of the MOP at the tth iteration. C_Iter indicates the
current iteration. M_Iter indicates the maximum number of
iterations. ais an adjusting parameter used to specify the
exploitation accuracy. Throughout iterations, which is set
equal to 5.
3.2.3 Exploitation phase
The MOA function value conditions this part of paper for
the condition of r1 is not greater than the current MOA(C
Iter) value.
xi;jC Iter þ1ðÞ
¼best xj
MOP UBjLBj
lþLBj
;r3\0:5
best xj
þMOP UBjLBj
mu þLBj
;otherwise
(
ð5Þ
3.3 Pseudo-code of AOA
The pseudo-code of the proposed AOA is reported in
Algorithm 1 (Fig. 2).
Fig. 2 Flowchart of the original AOA
Table 1 parameters data sets of the system
Cloud Entity Parameter Ranges
Nodes Number of datacenter [10, 100]
User Number of users [10, 1000]
Regions Number of regions [5, 25]
Geographical Geographical capacity [10, 128]
Bandwidth Bandwidth [2 Mbps, 128 Mbps]
Data sets Data set size [2G, 128G]
Data file Number of file [10, 5000]
Cost of file Cost of data replica [100, 6000]
Storage nodes Storage capacity [10, 128]
Transfer rate Maximum transfer rate [8, 256 MB/s]
Host Number of host [10, 200]
Processor Processing elements [12, 128]
MIPS MIPS [500, 2000]
Memory RAM RAM [2, 32G]
Virtual machine Number of VM [100, 800]
Processor Processing elements [8, 128]
MIPS MIPS [200, 2000]
Memory RAM RAM [2, 32G]
Cloudlet Number of cloudlet [1000, 5000]
Length of task Length of task [1000, 100000 MI]
A. A. Mohamed et al.
123
4 Proposed swarm intelligence for data
replication
This part explains the proposed strategy for selecting and
placing data replication via a fog node. Based on IoT via
fog computing for the proposed strategy, the shortest path,
cost, bandwidth, time, cost, and distance were calculated.
Use iFogSim to implement the proposed model.
4.1 Cost and time of replication
Cost is a primary factor in placement data replication close
to the users according to the user’s budget. The cost is
different from user to user through the proposed strategy
cost nodes are different from one fog node to another
according to the placement near the users. The Equa-
tion can be represented as follows:
cost DT j
¼X
n
y¼1
cost dty
z
ð6Þ
where
cost dtx
z
¼X
m
z¼1
xy
zðpy
zþsize dty
ðÞ
by
z
tcost ð7Þ
DTiCost of data set. dty
zData replica in region. xy
zA binary
decision variable q2ð1;2;3;...::lÞ.py
zPrice of replica. by
z
Bandwidth network between replicas in region.
4.2 Shortest paths problem (SPP)
between nodes based on the Floyd
algorithm
The Floyd algorithm obtains the shortest path between the
node in fog computing. Generally, when implementing the
Floyd algorithm to obtain the weighted length between the
ELSE
IF (r3>0.5) Exploitation phase
Update the ith solutions' positions using the first part in Equation (5).
ELSE
Update the ith solutions' positions using the second part in Equation (5).
ENDIF
ENDIF
ENDFOR
ENDFOR
C_Iter=C_Iter+1
ENDWHILE
Return the best solution (best_x).
Algorithm 1 Pseudo-code of AOA
Initia lize the Arithmetic Optimization Algorithm parameters N, α, μ, etc.
Initia lize the solutions’ positions randomly. (Solutions: i= 1... N).
WHILE (C_Iter < M_Iter)
Calculate the Fitness Function for the given solutions.
Find the best solution so far.
Update the MOA parameter using Equation (2).
Update the MOP parameter using Equation (4).
FOR (i=1 to Solutions)
FOR (j=1 to Positions)
Generate random values between [0, 1] (r1, r2, and r3)
IF (r1>MOA)
IF (r2>0.5) Exploration phase
Update the ith solutions' positions using the first part in Equation (3).
ELSE
Update the ith solutions' positions using the second part in Equation (3).
ENDIF
A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in...
123
shortest path among the DCs in fog computing. This paper
aims to solve the issue of selecting and placing dynamic
data replication across a geographically distributed node to
the shortest and optimal path in data transmission and
bandwidth (Cheng et al. 2014). The equations can be rep-
resented as follows:
first weighted adjacency matrixA¼ai;j½mmð8Þ
ai,j is a path from node ito node jmatrix m.
The state transition equation is as follows Eq.
map I;J½:¼min map I;k½þmap K;J½;map I;J½fg
ð9Þ
Map [I,J] demonstrste the shortest distance from Ito j.Kis
the breakpoint of exhausting Iand j.
4.3 Popularity degree of the data file
The popularity of a file is determined by frequent access
from users, especially in recent use. The file that is very
popular with users in recent times is the file that has been
identified, replicated, and placed between DCs. The
Equation can be represented as:
PDi¼aniwi(10)
Each file’s replication factor (RFi) is calculated based on
the popularity degree as in Eq. (11).
RFi¼PDi
RNiFSi
ð11Þ
The dynamic threshold (TH) value is calculated as in
Eq. (12).
DH ¼min 1 aðÞRFsystem max 8
k2½1;2;...:; lRFk
;a20;1½
ð12Þ
PDipopularity degree, aninumber of access, witime based
forgetting factor. RFireplica factor, RNinumber of replica,
FSisize of data file
4.3.1 System-level availability
SBER is the system’s overall high availability. Users
should be able to access all files via tasks for data repli-
cation. Due to frequent user access, access to the most
popular files. SBER keeps the file accessible and well-liked
throughout the entire system. The following is a repre-
sentation of the Equation:
SBER ¼Ps
i¼1ðankxðPnk
j¼1bsjÞxPðFAkÞÞ
Ps
i¼1ðankxPnk
j¼1bsj
Þ
ð13Þ
4.4 Placement of new replicas
Placement dynamic data replica between nodes for optimal
selection of minimum distances. When placement data
replication between DCs, it considers the optimal minimum
path and low cost for users. It can also be represented as:
brkdci
ðÞ¼ RFkdci
ðÞ
Ps
i¼1RFkdci
ðÞ
xbrkaddðÞ
ð14Þ
4.5 Salp swarm algorithm
Recently, the SSA (Mirjalili et al. 2017) has been used and
inspired by the conduct of a swarm of salps in oceans.
Algorithm SSA is inspired by swarm intelligence, simu-
lating the integration between SSA and nature. It consists
of two main groups: leaders and followers. First, the
leaders do optimal fitness. The rest is followers. Second,
the fitness value is calculated for each algorithm’s optimal
solutions. The optimal solution found is called a leader.
The equations can be represented as follows:
1) Leader Phase: The leader location is modernized
using the following Equation:
X1
j¼Xbj þc1ubjlbj
c2þl
if c3[0:5
Xbj c1ubjlbj
c2þl
otherwish
(15)
c1decreases through the iterations as.
Where
c1¼2eð4t
TÞ2(16)
X1
jandXbj repesent new placement. c2andc3random
variable from 0 to 1. ubjandlbjrefer the domin of serarch at
dimintion j
2) Followers Phase: To modernize the followers’
locations,
Newton’s law of motion is used, which defined as
Xi
j¼1
2gt2þx0t;i2 (17)
So, modernizing the procedure of followers can be
formulated as
Xi
j¼1
2Xi
jþXi1
j
ð18Þ
titeration. x0¼0andgvelocity and the acceleration.
4.6 Pseudo-code of SSA
The pseudo-code of the proposed SSA algorithm is
reported in Algorithm 2.
A. A. Mohamed et al.
123
4.7 Mean service time (MST)
MST describes the ability of a system to speed up
responses to users. When selecting the more popular files,
waiting for users reduces load balancing and minimum
bandwidth. The mean service time of file fi can be calcu-
lated by:
stfi¼X
m
j¼1
stf i;j
ðÞ
Aði;jÞ
AðiÞ
ð19Þ
The mean service time of the system can be defined as
follows:
mst ¼1
nX
n
i¼1X
m
j¼1
:£i;jðÞ
si
tpj
Ai;jðÞ
AiðÞ
!
ð20Þ
stf i;jðÞexpected service time of file in data node. Ai;jðÞ
access rate of read requests from data node. AiðÞ mean
access rate. sisize of file. tpjtransfer rate of data node.
4.8 Computational complexity
Calculate the time complexity of the proposed strategy
AOASSA from tasks for m number of data repetitions.
Calculate the number of num_DCs and AOA with SSA.
Suppose N represents the size of the population, D
Fig. 3 Cost number of tasks
Fig. 4 Mean service of time for tasks
Fig. 5 Mean service of time for file
Fig. 6 Execution time for file
Algorithm 2 Pseudo-code of SSA algorithm
Initialize the population's n=1, 2, 3, 4 …... m. and the total number of generations (tmax).
Construct the initial set of N solutions X.
while (final condition is not satisfied) do
For each Xi Calculate the fitness of each search and determine Xb.
update c1 in Eq. (16)
for each i to n do
if (i==1)
Update the placement of the leading in Eq. (15)
else
Update the placement of the follower in Eq. (18)
end
end
update upper and lower bounds of variables
end
return Xb.
A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in...
123
represents the number of objectives, T represents the
number of iterations, and CoF represents the cost of
function. The SSA algorithm has a calculated complexity
of O(T(D*N?CoF * N)). Based on the algorithm phase
AOASSA strategy, the time complexity is O (N). Hence,
the AOASSA total time complexity is O (N*T*C), so it is
O(N).
Fig. 7 Impact of data replication on that transmission nodes
Fig. 8 Impact of data replication on that transmission tasks
Fig. 9 Degree of imbalance
Fig. 10 Standard of load balancing
A. A. Mohamed et al.
123
Algorithm 3: the proposed algorithm AOASSA
Input: Regions, Datacenters, Data availability, Minimum distance between Regions, cost, time, MSE, SBER, Fog Nodes,
popularity data file, max_iter, population size, number of IOT tasks.
Output: select and placement data file replica optimal
trade-off between carbon and energy consumption optimal solution.
Begin
Initialize no. of IOT tasks
Initialize of the population;
Initialize of the population using the fitness function
Initialize availability and unavailability probabilities
Initialize replicas according to costs and time
Initialize distance between regions
Initialize popularity data file
Initialize data replication costs and time
Initialize optimal best data replica placement in DC solution
Initialize least cost path
Initialize SBER
Initialize RF
Initialize budget
Initialize MST
repeat
Initialize the Arithmetic Optimization Algorithm parameters N, α, μ, etc.
Initialize the solutions’ positions randomly. (Solutions: i= 1... N).
WHILE (C_Iter < M_Iter)
Calculate the Fitness Function for the given solutions.
Find the best solution so far.
Update the MOA parameter using Equation (2).
Update the MOP parameter using Equation (4).
FOR (i=1 to Solutions)
FOR (j=1 to Positions)
Generate random values between [0, 1] (r1, r2, and r3)
IF (r1>MOA)
IF (r2>0.5) Exploration phase
Update the ith solutions' positions using the first part in Equation (3).
ELSE
Update the ith solutions' positions using the second part in Equation (3).
ENDIF
ELSE
IF (r3>0.5) Exploitation phase
Update the ith solutions' positions using the first part in Equation (5).
ELSE
Update the ith solutions' positions using the second part in Equation (5).
ENDIF
ENDIF
ENDFOR
ENDFOR
C_Iter=C_Iter+1
ENDWHILE
Return the best solution (best_x).
Apply carbon in Eq. ()
Apply energy aware in Eq. ()
Apply Salp Swarm Algorithm to EO
t++
End while
Calculate the RF
Calculate distance between regions
Calculate SBER
Calculate the cost and time
Calculate MST
Return the optimal minimum data replica placement in the region.
A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in...
123
5 Experimental evaluation
5.1 Configuration details
AOASSA select and placement dynamic data replication
between nodes. The proposed strategy has been imple-
mented on iFogSim. In this section, we will discuss the
setting, experimental result and configuration of fog com-
puting for the proposed strategy in detail (Table 1).
5.2 Results and discussion
5.2.1 Selecting the optimal data file
In Fig. 3, 100 to 5000 cloudlets are created on data repli-
cation crosses nodes and access to it. The proposed algo-
rithm proved to be the least cost path than the proposed
algorithm in terms of accessing nodes through the least
distance of fog cloud.
Figure 4creates from 1000 to 5000 tasks to reach data
replication from the minimum path and low cost. The
proposed strategy proved to be speed response to users than
other algorithms.
Figure 5creates from 50 to 300 data replications to
placement from the minimum distance and cost. The
average service time in select and placement data replica-
tion across nodes is less than the algorithms. The proposed
strategy proved to be low mean service time to users than
other algorithms.
In Fig. 6, create from 50 to 300 data replication to speed
up file access across nodes in cloud computing. The pro-
posed algorithm proves faster access to files across nodes
than other algorithms. The proposed strategy proved to be
faster for data replication than other algorithms.
Figure 7, shows the rate of data file transfer across
nodes. As shown in Figures a, b, and c, the number of
nodes varies to transfer files by the lowest path. The pro-
posed strategy proved to be superior to other algorithms.
Figure 8shows the rate of data file transfer across nodes.
As shown in Figures a, b, and c, the number of tasks varies
to transfer files by the least cost path. The proposed strat-
egy proved to be superior to other algorithms.
5.3 Performance evaluation
5.3.1 Degree of balancing
Figure 9shows the degree of imbalance over the network
nodes to perform several different cloudlets at different
times. The proposed strategy minimizes the degree of
imbalance to a low level.
In Fig. 10 shows the standard of load balancing over the
fog nodes to perform several different tasks at different
times. The proposed strategy minimizes the standard of
load balancing to a low level.
In Fig. 11 shows the throughput over the tasks nodes to
perform several different tasks at different times. The
proposed strategy minimizes the throughput to a low level.
6 Conclusion and future work
This article proposes a novel hybrid metaheuristic algo-
rithm based on IoT in fog computing for select and
placement data replication. AOASSA is a hybrid AOA
with SSA to improve data transmission and the least cost
path between nodes in fog computing. Our proposed
strategy was compared with other algorithms in fog com-
puting. The proposed strategy enhancement the selection
and placement of data replication and choosing the least
cost path, throughput and standard of load balancing. The
results proved the superiority of the AOASSA strategy over
other algorithms according to data transmission, load bal-
ancing, and distance. In future work, work on improving
our strategy through modern algorithms from metaheuris-
tics. In the future, to reduce the least cost path, bandwidth,
and cost of data replication. The evaluation of other pri-
ority strategies, such as bandwidth, fault tolerance, and
enhancement QoS for the new algorithm, will be studied.
Acknowledgements The researchers would like to thank the Deanship
of Scientific Research, Qassim University for funding the publication
of this project.
Funding The authors have not disclosed any funding.
Data availability Data are available from the authors upon reasonable
request.
Declarations
Conflict of interest The authors declare that there is no conflict of
interest regarding the publication of this paper.
Fig. 11 Throughput
A. A. Mohamed et al.
123
Ethical approval This article does not contain any studies with human
participants or animals performed by any of the authors.
Informed consent Informed consent was obtained from all individual
participants included in the study.
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Authors and Affiliations
Ahmed Awad Mohamed
1
•Ashraf D. Abdellatif
2
•Alhanouf Alburaikan
3
•Hamiden Abd El-Wahed Khalifa
3,4
•
Mohamed Abd Elaziz
5,6
•Laith Abualigah
7,8,9,10,11,12
•Ahmed M. AbdelMouty
13,14
&Alhanouf Alburaikan
a.albrikan@qu.edu.sa
&Laith Abualigah
aligah@ammanu.edu.jo
Ahmed Awad Mohamed
Ahmedawadmohamed2000@gmail.com
Ashraf D. Abdellatif
ashraf_abdellatif@yahoo.com
Hamiden Abd El-Wahed Khalifa
hamiden@cu.edu.eg
Mohamed Abd Elaziz
abd_el_aziz_m@yahoo.com
Ahmed M. AbdelMouty
a_abdelmouty@zu.edu.eg
1
Information System Department, Cairo Higher Institute for
Languages and Simultaneous Interpretation, and
Administrative Science, Cairo, Egypt
2
Department of Technological Management and Information,
Higher Technological Institute, 10th of Ramadan, Egypt
3
Department of Mathematics, College of Science and Arts,
Qassim University, Al-Badaya, Saudi Arabia
4
Department of Operations Research, Faculty of Graduate
Studies for Statistical Research, Cairo University, Giza,
Egypt
5
Faulty of Computer Science &Engineering, Galala
University, Suez, Egypt
6
Department of Mathematics, Faculty of Science, Zagazig
University, Zagazig, Egypt
7
Center for Engineering Application & Technology Solutions,
Ho Chi Minh City Open University, Ho Chi Minh, Vietnam
8
Hourani Center for Applied Scientific Research, Al-Ahliyya
Amman University, Amman 19328, Jordan
9
Computer Science Department, Prince Hussein Bin Abdullah
Faculty for Information Technology, Al Al-Bayt University,
Mafraq 25113, Jordan
10
Faculty of Information Technology, Middle East University,
Amman 11831, Jordan
11
Applied Science Research Center, Applied Science Private
University, Amman 11931, Jordan
12
School of Computer Sciences, Universiti Sains Malaysia,
11800 Gelugor, Pulau Pinang, Malaysia
13
Information System Department, Faculty of Computers and
Information, Zagazig University, Zagazig, Egypt
14
Vice Governor of EL-Sharqia, Zagazig, Egypt
A. A. Mohamed et al.
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