Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 638~650
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp638-650  638
Journal homepage: http://ijece.iaescore.com
Location-aware deep learning-based framework for optimizing
cloud consumer quality of service-based service composition
Alshaimaa M. Mohammed1
, Samar Shaaban Abdelfattah Haytamy2
, Fatma A. Omara3
1
Computer Science and Mathematics Department, Faculty of science, Suez Canal University, Ismailia, Egypt
2
Department of Computer Science, Faculty of Computer and Information, Fayoum University, Faiyum, Egypt
3
Computer Science Department, Faculty of Computer and AI, Cairo University, Cairo, Egypt
Article Info ABSTRACT
Article history:
Received Jul 8, 2021
Revised Jul 15, 2022
Accepted Aug 2, 2022
The expanding propensity of organization users to utilize cloud services
urges to deliver services in a service pool with a variety of functional and
non-functional attributes from online service providers. brokers of cloud
services must intense rivalry competing with one another to provide quality
of service (QoS) enhancements. Such rivalry prompts a troublesome and
muddled providing composite services on the cloud using a simple service
selection and composition approach. Therefore, cloud composition is
considered a non-deterministic polynomial (NP-hard) and economically
motivated problem. Hence, developing a reliable economic model for
composition is of tremendous interest and to have importance for the cloud
consumer. This paper provides “A location-aware deep learning framework
for improving the QoS-based service composition for cloud consumers”. The
proposed framework is firstly reducing the dimensions of data. Secondly, it
applies a combination of the deep learning long short-term memory network
and particle swarm optimization algorithm additionally to considering the
location parameter to correctly forecast the QoS provisioned values. Finally,
it composes the ideal services need to reduce the customer cost function. The
suggested framework's performance has been demonstrated using a real
dataset, proving that it superior the current models in terms of prediction and
composition accuracy.
Keywords:
Cloud service composition
dimensional reduction
Deep learning
Location-aware
Quality of service
This is an open access article under the CC BY-SA license.
Corresponding Author:
Alshaimaa M. Mohammed
Computer Science and Mathematics Department, Faculty of science, Suez Canal University
Ismailia Governorate, Egypt
Email: shaimaa_mostafa@science.suez.edu.eg
1. INTRODUCTION
Cloud computing is a network-based system in which information technology and computing
resources such as hardware, operating systems, storage, networks, databases, and even whole applications are
conveyed to users as on-demand facilities just through the internet. Cloud computing displays several
benefits, such as dynamic environment, on-demand services, scalability, yet additionally has several
challenges that ought to be considered by experts such as privacy and security, virtualization, scheduling,
resource discovery, reducing the attacks, service discovery, data replication, service recommendation, service
composition, and selection. Numerous businesses, including Amazon, IBM, Microsoft, and Google offer
several services for cloud computing [1], [2]. Subsequently, cloud computing offers a pool of on-demand
computing resources to users to find their needed service(s) that fulfill their request. As per National Institute
of Standards and Technology (NIST) definition, cloud computing has five essential characteristics:
on-demand self-service, broad network access, resource pooling, rapid elasticity, and admeasured services. It

Int J Elec & Comp Eng ISSN: 2088-8708 
Location-aware deep learning-based framework for optimizing cloud … (Alshaimaa M. Mohammed)
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is deployed according to three deployment models: public, private, and hybrid models. Furthermore, software
as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are the three service
categories it provides [3], [4]. These services draw in more taking into account scaling up and down,
dependable resources for high-performance hardware and software, as well as eliminating security risks and
maintenance costs. Accordingly, numerous organizational leaders and Information technology (IT)
organizations’ stakeholders have urged moving to cloud computing.
Over the last few years, with IT improvement, conventional services cannot accomplish the demand
of users. Henceforth, blending IT and services is commendable for present-day intelligent systems. Users can
carry out their requirements identified with education, transportation, commerce, social networking, and
numerous other utility cloud services [3], [5]. The foremost critical objective for users is to effectively utilize
their requested services for lower operational cost, increasing scalability, improving performance, and
utilizing their resources in efficient ways. Subsequently, cloud computing has developed to meet users’
computing prerequisites by advertising new features designed to enhance the sharing of computing resources
and applications on demand and based on pay-as-you-go through the internet [2], [6].
As a result of the presence of complex and diverse services, a single simple service cannot fulfill the
existing functional requirements for many real-world cases [7]. To complete a complex service, it is essential
to have a combination of simple services that work with each other. In this manner, there is a strong need to
embed a cloud service composition system in cloud computing which has pulled in numerous researchers’
inspection [8]. Each cloud service has its own quality of service (QoS) parameters like response time,
throughput and cost which depicts the non-functional attributes of the service. As the quantity of services is
expanded in the cloud, various services can meet the users’ request functionality. Yet, these services are
varying in their QoS values. In this way, the service selection decision-making process brings a major
challenge in the service composition process. Thus, choosing the proper and optimal simple services that
would be consolidated to provide composite complex services is one of the most important challenges in
cloud service composition. The cloud service composition problem in cloud computing can be characterized
as figuring out what atomic simple services ought to be chosen with the acquired complex composite service
fulfills both the functional and QoS requirements based on the consumer requirements. Considering different
and plentiful effective parameters and countless simple services provided by many service providers in the
cloud pool, the problem with cloud service composition is a combinatorial problem of optimization and also
has been viewed as a non-deterministic polynomial (NP-hard) problem [8].
Subsequently, cloud service composition is a method for creating with fundamental meta-services
features for a complicated composite service that satisfies the functional needs of the consumers. It can be
relatively straightforward if each function’s meta-service is distinct. There is no standalone cloud service,
which is the fundamental fact of the cloud environment [9]. Likewise cloud computing innovation’s
pay-as-you-go feature enables service providers to offer their services in a variety of configurations in
accordance with service level agreements (SLA) [10]. In this way, the increase in the number of meta-
services diverse cloud providers poses a problem. The functional characteristics of these services are similar,
but their non-functional actions differ (i.e. QoS) [11]. Accordingly, when different functionally overlapping
services are supplied at different QoS levels, cloud consumers will find it difficult to choose the right
services. Consequently, there is a pressing demand for cloud service composition models.
There are more research concerns about solving the problem of selecting the most appropriate
services for the QoS-based cloud service composition [12], [13]. Notwithstanding, a large portion of this
research focuses on static QoS values observed during composition time. In practice, QoS varies over time,
such future variations should be thought about while designing composite services.
Contrasted with conventional composition of services, composition process is typically focused on
long-term goals and economics. Conventional techniques for composition that are focused on quality
typically take the attributes into account when creating the composition [14]. Which composite service, for
instance, performs the best right now? This is fundamentally different in cloud environments since the cloud
service composition in these environments is meant to last for a long time. What hybrid cloud service, for
instance, will perform best in the coming years even though it may not be the greatest one right now?
Multiple study fields, including multimedia, statistics, and others, frequently use time-series databases. To
examine economic models successfully and efficiently in cloud computing, numerous methodologies have
been developed.
There is now a solution to the composition of the QoS-aware services issue using a number of
strategies [13], [15], [16]. These methods ignore the volatility in the cloud environment and instead focus on
service quality at the time of composition, which does not consent to the commonsense circumstances.
Nowadays, enormous enterprises and with cloud service providers, institutions are typically more eager to
establish long-term commercial relationships. Be that as it may, the consumers’ actions (i.e., QoS
specifications) and the effectiveness of the chosen services (i.e., QoS characteristics) are essentially unstable.
Accordingly, the composite service with the top QoS performance at the moment is not an awesome

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timeframe. So compared to regular online services, The cloud service composition is thought of as an
economic strategy based on the long term. Typically, the economic model of a cloud consumer seeks to
achieve things like increased throughput, decreased reaction times, and low capital costs. We presume that
QoS features are expressed as a time sequence in this research. The correlation between the QoS
characteristic and one or more other QoS attributes is possible. In study [17], when the correlation operator
between QoS attributes is put to the test, it is found that throughput and response time attributes have a very
significant negative correlation. By taking into account all of the connected time series, the correlation factor
between QoS characteristics can be used to forecast the time series of certain QoS qualities. The majority of
QoS-aware prediction models that are currently available do not take the correlations between QoS variables
into account. Then again, because of economic considerations, enormous companies and organizations
generally are more able to construct a long-term business relationship with cloud service providers. On the
other hand, the end user’s requests (i.e., QoS requirements) and the selected services’ performance (i.e., QoS
attributes) are both fluctuant in practice. Accordingly, the composite service with the best current QoS
performance is not necessarily the best after a while or is not awesome sometimes. Thus, cloud service
composition systems provide a value-added service.
To tackle the problem of composing the best cloud services that cut down on the function of
consumer costs, this paper’s work provides a location-aware framework based on deep learning for
improving cloud consumer QoS-based service composition location-aware deep learning-based service
composition (L-DLSC). This framework is an enhanced framework based on our earlier work [1]. It
addresses the problem as a multivariate time series analysis. The proposed L-DLSC framework comprises
three phases. As per the primary phase, the filtration phase is acquainted and applied to select the best-
contributed attributes to reduce the total execution time of learning and composition. In the subsequent phase,
the long short-term memory (LSTM) network is used to accurately predict future behavior. In the third phase,
the composition of the service is handled as a multi-objective problem using the particle swarm optimization
algorithm (PSO) with considering the location between services and the consumer.
Therefore, this paper’s contribution can be summed up as: i) by combining the deep learning
network LSTM and the PSO algorithm, a framework to compose services using deep learning-based service
composition (DLSC) has been improved; ii) to speed up execution, the number of existing characteristics has
been decreased utilizing an autoencoder (AE) network. When choosing the best composite service to reduce
latency, the location parameter is taken into account.
The remaining portions of the essay are structured; the fundamentals of cloud service composition
are introduced in section 2. The related work is presented in section 3. Section 4 introduces the proposed
location-aware deep learning-based cloud service composition architecture. Section 5 presents the
experimental findings. Section 6 presents conclusions and ideas for additional research.
2. METHOD
Utilizing a combination of cloud services is intended to provide value-added services can complete
demanding tasks for customers. The consumer’s QoS limitations and preferences must be met as one of the
service composition’s requirements [1]. The choice of the best individual services that must be combined to
provide value-added composed services is one of the main issues with the service composition challenge
[18]. Mathematically, the multi-dimensional, multichoice knapsack problem (MMKP), an NP-hard problem,
can be used to represent the composition of a QoS aware service. As a result, the composite service (CS)
problem, which was mentioned in our earlier work, may be characterized as being the make-up
classifications of the essential services (SC), which are depicted by (1) [1].
𝑆𝐶 = [𝐶1, 𝐶2, … , 𝐶𝑗, … , 𝐶𝑛] (1)
With C stands for a particular a solitary service class, n stands for the necessary quantity classifications of
individual services, the (2) defines a single service class called 𝐶𝑗.
𝐶𝑗 = {𝑆1, 𝑆2, … , 𝑆𝑓} (2)
Where, the single service class 𝐶𝑗 contains f (f>1) functionally equivalent services with different QoS values.
So, the quality of SC can be determined using (3).
𝑄𝑜𝑆(𝑆𝐶) = [𝑄𝑜𝑆(𝐶1), 𝑄𝑜𝑆(𝐶2), … , 𝑄𝑜𝑆(𝐶𝑗), … , 𝑄𝑜𝑆(𝐶𝑛)] (3)

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Finding the best composited services with the best QoS characteristics to satisfy the needs of cloud
consumers is a clear objective in the selection process. Cost, throughput, and reaction time are taken into
account as QoS features in this study.
For the cloud service composition, which regularly changes in accordance with the complex job
specification and its needs, there have been a number of necessary workflows. Sequential pattern, conditional
pattern, loop pattern, and parallel pattern are the four fundamental composition patterns (e.g., workflow
patterns) that can be used to construct a composite service. The aggregation functions of various QoS
parameters vary often for each composition pattern. Therefore, as stated in our earlier work, Table 1 displays
the composition pattern and its accompanying aggregated functions. Ought to be taken into consideration to
specify and assess the general level of the cloud composite service quality as shown in Figure 1 [1], [14],
[15].
Table 1. Aggregation functions, where k is the number of loop times, p is a probabilistic variable, and n is the
total number of composing services [19]
Cost Response Time Throughput
Sequential
∑ 𝑄𝑐𝑜𝑠𝑡(𝑖)
𝑛
𝑖=1
∑ 𝑄𝑟𝑡(𝑖)
𝑛
𝑖=1
(𝑄𝑡ℎ(𝑖))
𝑖=1…𝑛
𝑚𝑖𝑛
Parallel
∑ 𝑄𝑐𝑜𝑠𝑡(𝑖)
𝑛
𝑖=1
(𝑄𝑟𝑡(𝑖))
𝑖=1…𝑛
𝑚𝑎𝑥
(𝑄𝑡ℎ(𝑖))
𝑖=1…𝑛
𝑚𝑖𝑛
Conditional
∑ (𝑃(𝑖) ∗ 𝑄𝑐𝑜𝑠𝑡(𝑖))
𝑛
𝑖=1
∑ (𝑃(𝑖) ∗ 𝑄𝑟𝑡(𝑖))
𝑛
𝑖=1
∑ (𝑃(𝑖) ∗ 𝑄𝑡ℎ(𝑖))
𝑛
𝑖=1
Loop K * 𝑄𝑐𝑜𝑠𝑡 (𝑖) K * 𝑄𝑟𝑡 (𝑖) 𝑄𝑡ℎ (𝑖)
Figure 1. Basic composition pattern [20]
The best way to solve the composite service problem is to maximize or minimize the vector’s
component values while maximizing the 𝑄𝑜𝑆𝑡𝑜𝑡𝑎𝑙 (𝑆𝐶) vector. The composing system is unaware of how the
final composited service will be delivered. will behave over an extended period of time because it has chosen
the concrete service providers for the composited service. As a result, it is crucial to foresee the long-term
desires of cloud users, which are illustrated by the economic framework. As opposed to that, economic
frameworks are described as “theoretical constructs that characterize economic processes by a collection of
variables and a set of logical and quantitative linkages between them”. The composition of cloud services
challenge is seen as an optimization issue with multiple objectives to boost reaction times, cut costs, and
increase throughput [12].
The customer’s actualized QoS values within a specific time period t, together with their
corresponding weights, a scoring feature S indicated using the common weighted score function [4]:
𝑆(𝑡) = 𝑊𝑡ℎ(𝑡) ∗ 𝑞𝑡ℎ(𝑡) + 𝑊𝑐𝑜𝑠𝑡(𝑡) ∗ 𝑞𝑐𝑜𝑠𝑡(𝑡) + 𝑊𝑟𝑡(𝑡) ∗ 𝑞𝑟𝑡(𝑡) (4)
As a result, for the purpose of creating a composite plan, the score function, which is established using, the
(5) is the total contains all QoS ratings for the full time T.
𝑆(𝑝𝑙𝑎𝑛) = ∫ 𝑠(𝑡)𝑑𝑡
𝑇
0
(5)

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Since there are numerous cloud infrastructure and software suppliers offering various combinations.
These mixtures result in different plans. Finding the composing services that maximize S(plan) is the primary
goal of the cloud service composition. The research presented in this essay suggests a location-aware
framework based on deep learning to effectively forecast recommended services that optimize the consumer
objective function in order to increase composition accuracy.
3. RELATED WORK
Problems with the composition of cloud services is split into two groups: both functional and
non-functional characteristics, or QoS. Consumers can differ in their preferences, requirements, and actions.
As a result, approaches to composition that are QoS-based, long-term, and location-aware are necessary.
3.1. QoS cloud service composition
Asghari et al. [16] have offered an approach for internet of things (IoT) environments for providing
composite cloud services. This approach classifies the candidate services into four categories based on their
QoS fitness value and privacy-preserving level. To determine the QoS fitness value for each candidate
service, an SFLA-GA hybrid algorithm is proposed of genetic algorithm (GA) and shuffled frog leaping
algorithm (SFLA) based on nine QoS parameters (i.e., availability, response time, reliability, throughput,
latency, compliance, success ability, documentation, and best practice). These nine parameters are
normalized and aggregated together to determine the fitness value for each candidate service. On the other
hand, the privacy level is determined by considering three factors (i.e., sensitivity, delegation depth, and
retention time). Finally, the candidate service is belonging to one of four categories: low fitness/low privacy
level, low fitness/high privacy level, high fitness/low privacy level, and high fitness/high privacy level.
Finally, the authors have supposed that the last category contains the most proper services for the consumers’
request.
In study [13] uses an eagle strategy to develop a QoS-aware cloud service composition. In this
approach, exploration and exploitation are balanced in order to address problems like poor convergence rate.
The interdependencies and correlations between cloud services as well as QoS parameters are not taken into
account by this approach. The authors presumptively used a single repository for all services. As a result, it
also ignores the necessity to compose services from a variety of service repositories while limiting
connectivity across the various clouds. An algorithm for QoS-aware cloud service composition based on
discrete immune fruit fly optimization algorithm (DIFOA) is proposed in [12]. According to this algorithm,
the initial population is generated which consider as the initial optimal weights for the resultant services from
Pareto applied on initial candidate services. At that point, it finds an optimal composed service based on user
preferences attains good fitness value, execution time, and error rate. Since the workflow and concrete
services were similar in terms of range, the effect of larger variations in the request-workflow pairs is not
considered in this algorithm. Additionally, it does not consider the different selection matching functions.
The composition problem is addressed using integer linear programming (ILP) in [21]. It converts
the restrictions and objectives of the issue into linear equations, which are then solved by modifying a special
software solver like LPSolve. Surprisingly, as the size of the problem grows, the integer program technique
requires exponential time. In order to address the time cost shortage issue, the service composition problem is
solved using a GA, in which each chromosomal gene represents a composite service's abstract service and
value denotes an applicant services [22]. A strategy based on GA conducts quick seeks to discover almost
ideal answers. Nevertheless, because it takes into account static levels of QoS at the moment of construction,
not at all well suited for cloud service composition.
The matrix factorization of the user-service matrix is used to leverage the predicted future QoS for
planning the composite service in [23]. The grid of user services is then divided into the sum of the user and
service matrices using the singular value decomposition method. However, this paradigm has relied on quick
QoS values. To maximize resource consumption, the network of deep recurrent neurons is how to predict
future QoS for cloud service providers [15]. It is regarded as having just one QoS feature. As a result, it is
unable to take into account the relationships among various QoS parameters.
3.2. Long-term service composition
The study [15] proposes a deep learning strategy for long-term QoS-based service composition. It
uses deep recurrent long short-term memories (LSTMs) to predict long-term QoS trends. It provides a
composition middleware that sends two types of recommendation requests to the long-term QoS compliance
checker (LQCC); composition and substitution requests. A composition recommendation request includes the
ID of a potential component along with QoS requirements to LQCC. LQCC requests the QoS prediction
trend for the component from the QoS predictor. Then, it checks compatibility between the QoS prediction

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and QoS requirement intervals and returns a composition recommendation to the middleware. A substitution
recommendation request includes IDs of the services to substitute and potential substitutes to LQCC. LQCC
obtains the QoS prediction trends for the component to substitute and potential substitute from the QoS
predictor. Then, it checks whether the two trends are close enough to each other and returns a substitution
recommendation to LQCC.
A framework for DLSC was put out in [1]. This framework works by combining both the PSO
technique and the deep learning LSTM network to forecast the QoS values. The long-term based QoS service
composition has been formulated in another model as an optimization issue employing meta-heuristic
methods such as genetic algorithms, annealing, and tabu search [24]. This model shows that only the most
effective services with the typical long-term QoS are chosen. The relationships among the QoS measures,
however, have not been taken into account.
In [25], ARIMA or the auto-regressive integrated moving average model to forecast how service
requests will behave going forward. It is not appropriate for long-term composition designing because it has
not collected stochastic request arrivals. In [26] defines a sustained qualitative approach that cloud service
providers must compile customer requests. It saw the IaaS composition as an optimization problem including
preferences.
3.3. Location-aware service composition
The presentation of a unique location-aware collaborative filtering approach for QoS-based web
service recommendation in [27]. First, it considers the individual QoS traits of both services and consumers
to calculate the likeness between them based on using Pearson’s correlation coefficient (PCC). Then, it
obtains location information of a user including the network and the country according to the users and
services and finds the similarity between them. Finally, according to the QoS and location similarities, the
required service is recommended.
A location-aware service recommendations with privacy-preservation for the internet of things (IoT)
environment is proposed in [28]. First, the QoS matrix is divided for recommendation decision-making into
multiple QoS sub-matrices based on service location information. Second, the neighbors of a target user are
found through a less-sensitive manner according to the QoS sub-matrices and the locality-sensitive hashing
(LSH) technique. Finally, the appropriate candidate services to the target user are recommended with the help
of derived neighbors.
4. PROPOSED LOCATION-AWARE DEEP LEARNING-BASED SERVICE COMPOSITION
FRAMEWORK
Building precise future economic strategies is key to the cloud business’ success. The work in this
paper describes the introduction of a location-aware framework for the composition of services using
deep learning L-DLSC based on reducing the space dimensions of the multiple time series and combining the
PSO and LSTM networks are used to create an economic framework and choose the best service providers
for the clients. Three phases make up the suggested model’s flowchart as shown in Figure 2. In the sections
that follow, the phases of the proposed L-DLSC framework will be thoroughly covered.
Figure 2. The suggested structure for service composition
4.1. Data preprocess and reduction phase
In this phase, the QoS data will be preprocessed and normalized. Then, their dimensions are reduced
using an autoencoder network. The main goal of this phase is to reduce the execution time required for
predicting the long-term QoS data.

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4.1.1. Data preprocess
As described in our previous work [1], there are three data preprocessing stages used to increase the
forecasting accuracy. In accordance with the first step, noise is removed from the QoS time series by using
the wavelet transform. Data normalization is necessary in the second stage and is done by applying (6) to
scale converting information from the original space to actual numbers between 0 and 1.
𝑦 =
𝑥−𝑚𝑖𝑛
𝑚𝑎𝑥−𝑚𝑖𝑛
(6)
Where the observation’s initial value (x) and its new scaled value (y) are represented, respectively, and min
and max represent the low and high points of the series. At stage three, in order to transform QoS information
into supervised machine learning form (X, Y), where X represents patterns for input and Y represents patterns
for output/label, it must first be converted using time-series format, which is a list of observational metrics
beginning with x1, x2, ..., xn. The shift function in this phase is used to advance and regress the observations
using the anticipated/next observations and the planned duration of lagged observations (X), (Y).
4.1.2. Dimensionality reduction
Many dimensionality reduction methods, including the discrete Fourier transform (DFT) and
singular value decomposition (SVD), have been proposed for the transformation of time series data [29],
principal component analysis (PCA) [30], discrete wavelet transform (DWT) [31] and piecewise aggregate
approximation (PAA) [29]. Time series segmentation is a different method for dimensionality reduction [32].
These methods now in use automatically select the amount of hidden variables to be used while also
identifying hidden variables among n input streams. According to their distributions and correlations, the
observed values of the hidden variables display the general pattern of various input series. Due to the lack of
a well-defined similarity measure, these current methods cannot be easily expanded for the tags similarity
search problem. Most crucially, the relationships between the various time series cannot be handled by
present techniques.
Recently, the autoencoder network is widely used for dimension reduction. Autoencoders are simple
learning networks that aim to transform inputs into outputs with the least possible amount of distortion [33],
[34]. As shown in Figures 3 and 4, Autoencoders are artificial neural networks (ANN) with symmetric
structure, where the middle layer represents an encoding of the input data [35]. They are trained to
reconstruct their input onto the output layer while verifying certain restrictions which prevent them from
simply copying the data along with the network [36]. Starting with a short code that ignores noise,
Autoencoders condense the original data. The code is then uncompressed to create an image that is as near to
the original input as feasible. According to the work in this paper, the Autoencoder network has been
constructed to reduce the search dimensions.
Figure 3. The autoencoder network [34]

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Figure 4. visualization description of autoencoder [37]
4.2. Long term prediction
In our earlier work [1], we showed how extended sequences may be learned and predicted using
LSTM. The forget gate, input gate, update gate, and output gate are the four components that make up an
LSTM memory cell [26]. These gates regulate the interactions between memory cells that are close to one
another as well as the memory cell itself. The status of the cell is compared to a conveyor. The unrolled
LSTM network and the method for updating each gate value are shown in Figure 5. Where, at time t, the
memory cell’s input vector (xt) and output result (ht) are, respectively, the input vector and result. The input
gate’s it, forget gate’s ft, and output gate’s ot values, respectively, are those at time t. it, ft and ot are the values
of the input gate, the forget gate, and the output gate at time t, respectively. The memory cell’s potential state
at time t is denoted by ̃Ct.
According to our experiments, the layer LSTM, the layer’s density and dropout procedure make up
the majority of the prediction model architecture as shown in Figure 6. First, input matrices comprised of
four lagged the LSTM layer receives four next values/look ahead values in addition to values for each QoS
attribute (X) (Y). The dropout technique is then used on the output of the LSTM layer to avoid over-fitting the
DLSC prediction model. The dense layer, a neural network (NN) that is fully linked and has a linear
activation function, is where the output from the dropout process passes after that. MSE is then determined as
the loss function. The “Adam” optimizer is used to optimize the model parameters in order to minimize the
loss function. The dropout parameter is adjusted to 0.2 following extensive studies to determine the optimal
way to train the network and obtain the best generalization scenario. The network will not exist without the
dropout.
Figure 5. LSTM cell [26] Figure 6. Flow chart of the L-DLSC prediction
model
4.3. Location-aware service composition
Location is a crucial factor for recommendation of decision making where the QoS often depends
heavily on the user locations or service locations. For example, a user in Australia may access a web service
hosted in Australia very quickly, but it is very slow to access a web service hosted in China [27]. Therefore,
location is a crucial factor that impacts the accuracy of composition results and the satisfaction of the user.
In this paper, the Haversine formula has been used to calculate the location factor [38]. The
haversine formula determines the great-circle distance between two points on a sphere given their longitudes
and latitudes. The law of haversines is important in navigation; it is a special case of a more general formula
in spherical trigonometry, which relates the sides and angles of spherical triangles. Therefore, the haversine
formula is considered an accurate way of computing distances between two points on the surface of a sphere

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using the latitude and longitude of the two points. The haversine formula is a re-formulation of the spherical
law of cosines, but the formulation in terms of haversines is more useful for small angles and distances.
ℎ𝑎𝑣(𝜃) = 𝑠𝑖𝑛2
(
𝜃
2
) =
1−cos (𝜃)
2
(7)
Where, θ is the central angle between any two points on a sphere, such that, θ=d/r, d is the distance between
the two points along a great circle of the sphere, r is the radius of the sphere. Therefore, during the
composition process using the PSO algorithm, the distance of the candidate services is determined using the
haversine formula as illustrated in the following algorithm [39].
Algorithm. Calculate distance
Inputs:
1. Lat1: customer location latitude in decimal
2. lon1: customer location longitude in decimal
3. lat2: candidate cloud service location latitude in decimal
4. lon2: candidate cloud service location latitude in decimal
Output: distance: distance between the customer and the candidate cloud service
R=6373 // radius of Earth
Convert Lat1,lon1,lat2, lon2 into degrees
dlon= lon2-lon1 //change in coordinates
dlat= lat2-lat1
//Haversine formula
a= math.sin(dlat/2)2
+ math.cos(lat1)*math.cos(lat2)*math.sin(dlon/2)2
C=2 * math.atan2(𝑚𝑎𝑡ℎ. 𝑠𝑞𝑟𝑡(𝑎), 𝑚𝑎𝑡ℎ. 𝑠𝑞𝑟𝑡(1 − 𝑎))
distance=R*c
Return distance
The services with a high distance value from the client are penalized under our methodology. The
following is a possible way to represent a penalty on service x in a certain candidate plan.
Penalty(X) = 1 −
distance(X)
∑ distance(xi)
n
i=1
(8)
Therefore, the objective function of this candidate plan is determined over the entire anticipated period T,
written as (9).
F(objective)=max{ (∫ s(t)dt ))
T
0
∗ ∏ Penalty(xi
n
i=1 } (9)
In a candidate constructed service plan, n denotes the total number of cloud services that are engaged.
5. RESULTS AND DISCUSSION
Several tests are conducted and contrasted with the existing framework in [16] and the earlier
framework provided in [1]. These tests are using the same environment setting in order to evaluate the
performance of the proposed service composition (L-DLSC) framework. The tests are being conducted on a
PC with a 2.80 GHz processor, 8.0 GB of RAM, and Windows 10 and Python 3.7 installed.
5.1. Data description
During our experiments, we adopted a popular real-world web service dataset, the web services
(WS-Dream) dataset [40]. This data set includes 339 users (from 31 countries) and 5825 services (hosted in
74 countries), as well as a historical time series QoS data for 100 cloud service providers, response time,
throughput, and cost metrics were gathered over the course of six months as 28 time slots [41]. The WS-
Dream dataset is used to conduct the IoT-existing framework while the later QoS dataset is used in our
previous framework. Here, all experiments are conducted using WS-Dream after adding cost attribute using
the same strategy as in [41].
5.2. Execution time
Figure 7 shows the comparison results among the proposed service composition L-DLSC
framework, the existing IoT framework in [16], and the previous DLSC framework given in [1] concerning
the execution time parameter. To conduct the cost-time analysis, Figure 7 highlights the value of time is
growing by increasing the quantity of composing services. Nevertheless, the current IoT framework [16]

647
whose execution duration is the longest in comparison to our proposed L-DLSC framework due to the
exhaustive task of decomposing the QoS-matrix into sub-matrices based on the user location then building
user indices based on the similarity measures between the user and the candidates’ services. Concerning the
proposed L-DLSC framework, a suitable penalty on the long distances between the candidate services and
the user is needed to locate the top writing services at a fair price. The typical time required to discover the
ideal composition plan using the proposed L-DLSC framework 50% more is added in comparison to our
previous DLSC framework, while the time cost of the IoT Composition framework is increased by 96%
relative to our previous DLSC framework. Therefore, according to the output of these experiments, the
proposed L-DLSC framework achieves significantly improved results for the time cost.
5.3. Optimality
In Figure 8, the values of the objective function in relation to the number of iterations are shown.
According to the results given in Figure 8, it is clear that our proposed L-DLSC framework converges faster
than the others, after which the current IoT framework [16]. Comparing our suggested L-DLSC framework to
the prior DLSC framework presented in [1], the average best objective F score is raised by 44%. Although
the average optimal objective F value utilizing the current IoT framework is higher than the proposed
L-DLSC framework by 32%. This is due to the proposed L-DLSC framework lessens the services’ weight
which with a high distance from the customer, while increases the weight for the services which have a low
distance from the customer. On the other hand, the existing IoT framework searches about the most similar
ones to find the excellent services came from the decomposed matrices that are considered a pricey
procedure. Besides, this proposed L-DLSC framework converges faster with higher composition values than
the previous DLSC framework presented in [1].
Figure 7. Execution time comparison Figure 8. Optimality comparison
5.4. Effect of dimensional reduction
According to the proposed L-DLSC framework, the first aim is to reduce the dimensions of the
composition problem. Therefore, the autoencoder network is added to perform this task. Figure 9 shows the
effect of adding the autoencoder network. According to Figure 9, the accuracy of the long-term prediction
process is compared after and before adding the autoencoder process in terms of the root mean squared error
(RMSE) where smaller RMSE means better performance. According to the results in Figure 9, it is found that
adding the autoencoder network significantly affects both the accuracy and the time required. Also, the
prediction error has been reduced to 17% after adding the autoencoder due to capturing the most important
features.
5.5. Effect of composing services aware of customer location
Figure 10 shows the comparison between our proposed L-DLSC framework and the previous DLSC
framework in [1]. The comparison is done after adding the location parameter to pick the best service. As
stated by findings in Figure 10, which demonstrate that the typical best objective F value using the new
proposed L-DLSC framework is increased by 44% relative to the previous DLSC framework in [1].
5.6. Composition output
The output of the suggested L-DLSC framework, or the best composition services, is shown in
Figure 11, where each number in the matrix corresponds to a service ID. Additionally, based on their
functional requirements, the impact for the average whole dataset using the DLSC framework is divided into

 ISSN: 2088-8708
648
three groups. Additionally, a straightforward design for service composition is taken into account, utilizing
three service classes that are continuously running throughout four periods to create the composite service.
Figure 9. Effect of adding autoencoder
Figure 10. Effect of the location parameter
Figure 11. Composition output
6. CONCLUSION AND FUTURE WORK
To reliably recommend cloud services based on QoS features, the location-aware framework for
service composition based on deep learning (L-DLSC) is presented in this work. First, by lowering the
dimensionality of the data, the Autoencoder network has been employed to narrow the search space. Then, a
combination of the deep learning LSTM network and PSO algorithm is used to solve multi-objective
problems while also leveraging the strength of the LSTM network when keeping track of time series'
multivariate QoS characteristics dependencies. Last but not least, the location factor has been taken into
account when choosing the best composite services. We may infer from the trials that have been done that
our suggested L-DLSC architecture significantly improves the outcomes in the cloud service composition
environment. Our proposed framework will be enhanced in the future by the inclusion of new elements like
security and privacy in the cloud composition issue.
0
0.05
0.1
0.15
0.2
0.25
0.3
50 100 150 200 250 300
RMSE
number of epoches
Proposed LDLSC our previous framework
0
1
2
3
4
5
100 200 300 400 500 600
achieved
objective
value
PSO iteration number
proposed LDLSC our proposed framework

649
ACKNOWLEDGEMENTS
This work is financially supported by the Academy for Scientific Research and Technology (ASRT),
Egypt, Science UP Grant No. 6646.
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BIOGRAPHIES OF AUTHORS
Alshaimaa M. Mohammed is a teacher at Faculty of Science, Suez Canal
University, Ismailia, Egypt where she obtained her BSc. In 2009 she completed Pre- Master
courses in computer at faculty of Science, Suez Canal University, Ismailia, Egypt. In 2014 she
completed her master’s degree. In 2019 she completed her PH. D degree. Her interests are
security and trust in cloud computing environment, IoT, and big data. She can be contacted at
email: shaimaa_mostafa@science.suez.edu.eg.
Samar Shaaban Abdelfattah Haytamy is an assistant lecturer in the department
of computer science, faculty of computers and information, Fayoum university, Egypt. Samar
received her MSc in computer science in 2020. Her research interests are machine learning,
federation learning, service-oriented computing, open-source software, cloud computing,
parallel programming, and big data. She can be contacted at email: Ssa10@fayoum.com.
Fatma A. Omara is a Professor in the Computer Science Department, Faculty of
Computers and Information, Cairo University. She has published over 120 research papers in
prestigious international journals, and conference proceedings. She has served as Chairman
and member of Steering Committees and Program Committees of several national and
International Conferences. She has supervised over 50 Ph.D. and M. Sc thesis. Prof. Omara is
a member of the IEEE and the IEEE Computer Society. Prof. Omara interests are parallel and
distributed computing, distributed operating systems, high performance computing, grid
computing, cloud computing, big data, IoT. She can be contacted at email: f.omara@fci-
cu.edu.eg.

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