Dynamic congestion management system for cloud service broker

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 872~883
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp872-883  872
Journal homepage: http://ijece.iaescore.com
Dynamic congestion management system for cloud service
broker
Tariq Alwada’n1
, Abdel-Karim Al-Tamimi2,5
, Adel Hamdan Mohammad3
, Maher Salem4
,
Yar Muhammad1
1
School of Computing, Engineering and Digital Technologies, Teesside University, Middleborough, United Kingdom
2
Computer Engineering Department, Yarmouk University, Irbid, Jordan
3
Computer Science Department, The World Islamic Sciences and Education University, Amman, Jordan
4
Department of Informatics, Faculty of Natural, Mathematical and Engineering Science, King’s College London, London,
United Kingdom
5
Department of Computing, Sheffield Hallam University, Sheffield, United Kingdom
Article Info ABSTRACT
Article history:
Received Mar 1, 2022
Revised Jul 18, 2022
Accepted Aug 19, 2022
The cloud computing model offers a shared pool of resources and services
with diverse models presented to the clients through the internet by an
on-demand scalable and dynamic pay-per-use model. The developers have
identified the need for an automated system (cloud service broker (CSB))
that can contribute to exploiting the cloud capability, enhancing its
functionality, and improving its performance. This research presents a
dynamic congestion management (DCM) system which can manage the
massive amount of cloud requests while considering the required quality for
the clients’ requirements as regulated by the service-level policy. In addition,
this research introduces a forwarding policy that can be utilized to choose
high-priority calls coming from the cloud service requesters and passes them
by the broker to the suitable cloud resources. The policy has made use of one
of the mechanisms that are used by Cisco to assist the administration of the
congestion that might take place at the broker side. Furthermore, the DCM
system is used to help in provisioning and monitoring the works of the cloud
providers through the job operation. The proposed DCM system was
implemented and evaluated by using the CloudSim tool.
Keywords:
Cloud broker
CloudSim
Congestion management
Service level agreements
This is an open access article under the CC BY-SA license.
Corresponding Author:
Tariq Alwada’n
School of Computing, Engineering and Digital Technologies, Teesside University
Middleborough, United Kingdom
Email: t.alwadan@tees.ac.uk
1. INTRODUCTION
Cloud computing, cluster computing, and grid computing models are designed to give access to a
vast amount of computing power by sharing information technology (IT) resources or services by running a
single or a group of system interfaces. Service computing is defined as IT resources (hardware or software)
where service providers establish those resources and provide them on demand [1], [2]. Service requesters
can pay the service providers on pay-per-use charging mode. It is essentially like a public service (such as:
gas, water, power, and telephone) in which the clients are charged on a daily or monthly basis as a pay-per-
use of the given unit [3], [4].
Cloud computing is an example of a model for offering on-demand network access to conﬁgure and
share computing resources. By now, cloud computing has spread widely in several applications and usages. It
becomes a significant part of the future generation of services and computing infrastructure at a reasonable

Int J Elec & Comp Eng ISSN: 2088-8708 
Dynamic congestion management system for cloud service broker (Tariq Alwada’n)
873
cost. This feature will enable cloud users to develop and operate their resources as a pay-per-use, as
described in Figure 1 [5]. Infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software as a
service (SaaS) are the models of cloud services offered by cloud providers such as online file storage, social
networking sites, and webmail [6].
Generally, the cloud service broker (CSB) is a software or a computer that works as a mediator
between two or more groups to discover and decide about the suitable resources for specific tasks, to send
input files of the jobs to the cloud resources, to monitor jobs, and to send the results of the job back to the
clients [7], [8]. Figure 2 illustrates the job of the CSB. In the cloud environment, the cloud requesters can
send their jobs to the cloud, specifically to the CSB to find suitable resources for them [9]. In this case, the
cloud broker holds a database that contains the available resources (physical devices and virtual machines) on
the cloud. The cloud can apply one or more algorithms to match the user jobs to the resources. These
algorithms can be forced by the CSB policy. Also, the CSB performs some other tasks such as monitoring,
controlling, provisioning, security, and substitution [9].
Figure 1. Cloud computing overview
Figure 2. Cloud service broker [9]
In addition, the cloud broker can have the right to discuss arrangements with the cloud providers on
the side of the cloud requester. In this situation, the CSB is given the power to split the job between many
service providers to reduce the cost as possible. Furthermore, the cloud broker can provide the cloud users
with an interface to hide some complexity and lets the cloud requesters deal with different cloud providers as
if they were being purchased from one provider. This type of broker is named a cloud aggregator [10].

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The architecture of CSB is shown in Figure 3 and its components are defined by Buyya et al. [11].
In this architecture, the CSB is divided into four layers:
− User interface: To offer the access connection between the broker and a user application interface. The
application interpreter defines what is to be executed from the user, the task ‘descriptions’, and the
desired quality of services. The service interpreter identifies the service prerequisites required for the
execution. These requirements include service type, service location, and some other necessary services.
The credential interpreter inspects the credentials for retrieving required services.
− Core services: This is where the main function of the broker takes place. The service negotiator receives
the service requirements from the user interface. The scheduler decides the most suitable cloud providers
for the user requested services based on its service and application requirements. The service monitor
continually monitors the condition of cloud services through a periodic checking of the availability of
recognized cloud services and searching for available new services.
− Execution interface: This offers execution provision for the user request. The dispatcher generates the
required broker-agent and attaches the data files along with the user job to be posted to the cloud
resources for implementation. The job monitor detects the execution level of the task to the results of the
task is departed to the user upon job completion.
− Persistence: This layer is very important in the case of broker failure. It keeps the state of the core
services, execution interface, and user interface in a database.
Figure 3. Cloud service broker architecture [11]
However, one situation that can be occurred when the broker rejects some requests if it reaches its
full capacity of cloud requesters. In this case, the broker may not be able to deal with such a huge number of
requests. The broker may (by the end) reject some requests to prevent the CSB from being overwhelmed. In
this paper, a new dynamic congestion management (DCM) system is created, which is derived from Cisco
queuing algorithms. This system can handle the huge amount of cloud requesters while considering the
customers’ quality of service conditions as regulated by the service-level agreement (SLA). Also, it is used to
monitor and provision the work of the cloud providers during the jobs running. There are many cloud-
computing models that have methods to provision and monitor their resources and define the cloud service
broker job. The following are the most standard models in the field of cloud computing and brokering
systems.
The dynamic resources provisioning and monitoring (DRPM) system proposed by Al-Ayyoub et al.
[12] is a multi-agent system designed to handle the resources in the cloud provider whilst taking into
consideration the required quality for the clients’ requirements. These requirements are controlled by the
SLA. DRPM system also comprises a new virtual machine with an algorithm named the host fault detection
(HFD) algorithm to select which virtual machines to move to if the hosted physical machine becomes
overloaded, varying on the source of the overload. The DRPM system was tested and evaluated using the
CloudSim tool. The results showed that the DRPM system has increased resource utilization and, at the same
time, has decreased power consumption by avoiding SLA violations.

875
Vecchiola et al. [13] proposed the Aneka system, which is a NET-based application (PaaS) for
cloud computing. This system offers a group of runtime environment applications and APIs by several
programming models and implements them on public and private cloud computing platforms such as GoGrid
and Amazon EC2. The Aneka system also offers resource monitoring methods built on SLA orientation. This
method runs as if the system accepts new tasks from cloud customers. It evaluates the time required to finish
these tasks, in turn, to complete them with existing resource and match it with the SLA deadline time. If the
estimated time to finish the new tasks is equal, then the system remains working; else, the system increases
the cloud resources and remains run to avoid any SLA violation.
Siddiqui et al. [14] proposed the Elastic-JADE system which has three elements (local machine,
Amazon EC2 cloud, and the cloud user). By using those elements, the system automatically balances
Amazon’s EC2’s resources (by scaling up or down the resources) via the Java agent development framework
(JADE) platform once heavy loads take place in the local platform. The agent at the client machine is in
control of provisioning the whole system on both sides. Also, it communicates with the administrative agent
in the Amazon EC2 then forwards directive orders for increasing or decreasing assets according to the system
load. Bonvin et al. [15] introduced the scattered autonomic resources (Scarce) model, which is a multi-agent
platform to dynamically administrate the resources by using an economic-based method. The agents work on
the server side and are responsible for directing and scaling the resources and constantly checking the
condition of the systems.
Venticinque et al. [16] proposed the open cloud computing interface (OCCI) framework, which
supports monitoring, provisioning, and auto-configuration for the cloud resources to fulfill the application
requirements at the infrastructure level (IaaS). The OCCI also contains a collection of API and protocols with
a self-contained supplier and neutral platforms, which resolve several problems in the administration of usual
tasks with the fulfillment of portability, interoperability, and integration requirements plus autonomic scaling,
monitoring, and deployment.
Morrison [17] considered three distinct types of CSB: i) customer-centric, created for customers’
service requirements and quality of service (QoS) providing to the customer; ii) solution centric, created for
collecting services presenting from a wide of technical services presented in the cloud. One of the main jobs
of this type is to ensure the integrity and the security of services; and iii) resource-centric, which works as a
service assembler. The author proposed that a service broker comprises self-service entitlement, application
catalogs, role-based access control, billing, SLA monitoring, metering, auditing, and reporting. He identified
the CSB as a business model that helps the customers to choose, manage, organize, and coordinate the
different services they require.
Praveen et al. in [18] introduced a new task scheduling and resource allocation schema for the cloud
environment. The proposed load balancing schema uses the social group optimization (SGO) algorithm and
consists of two autonomous phases: optimal resources allocation using SGO and effective scheduling of tasks
using shortest-job-first (SJF). Their experimental results have shown the SGO-based SJF scheduler has
reduced the makespan time and the number of active servers when compared to other first-come-first-serve
(FCFS) and genetic algorithm (GA) based schedulers, and thus reduces the associated cost of using cloud
services.
Guo et al. [19] introduced a load balancing schema for the edge cloud environments. The challenges
that edge cloud environments face are emphasized due to the high level of granularity of the required
resource billings and allocations processes. They proposed the use of auto regressive integrated moving
average (ARIMA) and back propagation (BP) neural networks to estimate the required load. Accurate
estimations can allow user data to be migrated to a less congested working node to ensure service continuity.
When compared to load estimation using ARIMA models and GA models, the proposed model outperformed
the other models in terms of load estimation accuracy. They also showed that using the proposed model could
reduce the associated service expenditures effectively.
In this research, the authors focus on identifying the need for an automated system CSB that can
help in utilizing the cloud power, enhancing its functionality, and improving its performance. The
contributions of this work can be summarized in the following points: i) presents a DCM system which can
manage the massive amount of cloud requesters while considering the required quality for the clients’
requirements as regulated by the service-level agreement; ii) introduces a forwarding policy that can be utilized
to choose high-priority requests coming from the cloud service requesters and passes them by the broker to the
suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to ease the
administration of the congestion that might take place at the broker side; and iii) furthermore, the DCM system
is used to help in provisioning and monitoring the work of the cloud providers during the job running.
The following are some backgrounds about cloud service models, the SLA, and the congestion
management algorithms. Figure 4 shows the cloud service models and the relations between them [20]. These
models can be explained as:

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− Infrastructure as a service: In this model, the provider is the holder of the equipment and tools. This
business model presents the virtualization of resources on-demand [21]. The providers give the cloud
users the ability to use those tools and equipment virtually instead of buying them. In this case, the
customer pays per use on-demand [3]. This model also allows the customer to perform self-provision
when using the ‘provider’s services.
− Software as a service: This is the most common model in cloud service. In this model, the client can
access the applications and use them without the need to download or buy them. Also, it is a storage and
supply model where the user obtains the storage area from the supplier on rent [22]. The users can use
and access SaaS services by using the web browser.
− Platform as a service: This is the service for operating applications over the internet by hiring software
and hardware structures from cloud providers [23]. It is mainly for application developers who can
develop and test their applications.
Figure 4. Cloud service models [20]
The clients under the cloud computing model do not have to worry about the problem of possessing
and running the physical base needed for their job. In other words, they do not have to be concerned about
programming teams and developers. Furthermore, the customers need not to concern about how their task
will be performed or where it will be applied. The only thing that they need to be concerned about is the
charge of using the resources, the quality of services promised, and the type of services they can acquire from
the cloud providers. The ones who are worrying about reaching efficient utilization and monitoring resources
are the CSB. By using smart ways and methods, the broker should obtain the finest quality of service assures
without affecting or creating any breach in the SLA [12]. A cloud SLA is an agreement between the cloud
providers and a cloud customer that ensures the lowest level of service is preserved. It guarantees levels of
availability, reliability, and responsiveness of applications and systems while determining who will have
control when there is a service interruption [24].
Because of the growing demands on cloud services, nowadays, applications and cloud service
requesters are asking to downgrade the completion time for their jobs while reducing the associated cost. At
the same time, they ask for the maximum QoS during their ‘jobs’ execution. Most of the requests now are
quite delicate for the delays faced when running and transferring over the internet. That is why it is required
to support a different type of traffic with different quality of services. The greatest significant side of this; is
how to distribute the available resources while facing congestion. To continue with this, it is required that
several methods and tools help to distinguish between the types of traffic coming to the cloud. This can be
done through prioritizing [25].

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This research introduces a forwarding policy that can be employed to choose high-priority calls
coming from the cloud service requesters (as an extra service) and deliver them by the cloud service broker to
the suitable cloud resources. So, there is a need to line up the low priority calls and hold them in the broker
memory till the high priority calls get handled by the cloud. This paper has made use of one of the
mechanisms that are used by Cisco to smooth the administration of the congestion that may take place at the
cloud service broker side. Cisco has many mechanisms which support the queuing on Cisco router interfaces
using hardware and software modules [25]. The chosen mechanism is the weighted fair queuing (WFQ). This
mechanism does not permit classification options to be configured. Instead, WFQ categorizes packets
automatically, with every flow being placed into a distinct queue according to its priority, and each one of
these queues has a specific weight [26]. The process starts as a round robin (RR) mechanism, where time
slices are allocated to each process in equal portions and circle order, but the processer allows the flow of
traffic to get through based on the weight of that queue in the round. In this case, the WFQ can solve the
starvation problem that could cause if the RR mechanism is being used. This paper is organized as: section 2
presents the proposed system model. The experiment and results are discussed in section 3. Finally, the
conclusions are presented in section 4.
2. SYSTEM MODEL
The details of the DCM system are explained in this part. The DCM system is a multi-agent model
that takes into consideration various elements when forming the decision, for example, the number and the
shortcoming of cloud resources, the customers’ fulfilment, and the customers’ QoS requirements. This
system is created by adapting and combing both the DRPM system introduced in [12] and the cloud broker
architecture introduced in [11]. Figure 5 shows the architecture of the DCM system. The model is split into
three parts: The cloud service users part, the CSB part, and the cloud service provider part.
2.1. The cloud service users part
In this part, a local agent is allocated to every customer. This agent is responsible for remarking the
customers’ requests based on their priority. This is considered as an extra paid service that the users can pay
for the cloud to guarantee the priority of their jobs over the cloud among other users. If this service is not
paid (exist), in this case, the local agent will consider the priority as a low priority. The local agent then sends
the marked requests to the CSB, precisely to the classifier inside the broker. At the user’s request, the job
specifications are determined, such as the type of virtual machine (VM), the hardware, and the software
required. Once a new call for a VM with particular features is collected from the client, the local agent marks
these requests as one of three cases: high, low, and medium. This will help the user to process some urgent
jobs where needed. The local agent can utilize the history of the user requests to help in marking the new
requests. The output of remarking step is sent to the classifier in the CSB for the next step.
2.2. The cloud service broker part
This part is the core of the cloud system and the DCM system. The element that is accountable for
getting the users’ jobs in the CSB is the classifier. The main job of the classifier is to recognize which job
should be processed first in the case of job congestion. As the CSB continues to receive jobs from different
cloud users, it may reach to reject some requests due to the huge number of requests. The DCM system plays
a very important role in this case. Instead of rejecting the requests randomly, the classifier can make use of
the jobs marking that has been done by the local agent on the cloud service user’s part to organize the
requests in a way that can take into account the QoS requirements as regulated by the SLA. If the CSB is not
congested the classifier applies the first in first out (FIFO) order for the incoming requests, but if the CSB got
congested, then the classifier depends on the WFQ mechanism to solve the congestion. WFQ categorizes
packets automatically, with every flow being placed into a distinct queue according to its priority, and each
one of these queues has a specific weight. The process starts as a RR mechanism, where time slices are
allocated to each process in equal portions and circle order, but the processer allows the flow of traffic to get
through, based on the weight of that queue in the round. The advantage of using the WFQ mechanism is that
it can solve the starvation problem that could cause if the RR mechanism is being used. Figure 6 shows how
the classifier deals with the incoming requests from cloud users. The classifier can make use of the Broker
Buffer in case of some requests need to be put on hold for further classification.
After the classifier decides which users’ requests should be processed first, it handles them to the
Scheduler for further process. The Scheduler decides the most suitable cloud services for the client requested
services built on its service requirements and application. The service monitor continually monitors the status
of cloud services through a periodic checking of the availability of recognized cloud services and searching
for available new services.

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Figure 5. Architecture DCM system
Figure 6. Classifier mechanism

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Later, the job dispatcher receives the user application (the user’s job) and attaches the data files with
the user application to be posted to the chosen cloud resources for implementation. The job monitor detects
the execution levels of the job to send back the results of the job to the user as soon as the job is finished.
This is done by checking the status of the job with the agent attached to the cloud resources. If the broker is
crashed for any reason, each element in CSB maintains its state in a database for recovery later. Finally,
when the job is accomplished, the agent attached to the cloud resource sends the results back to the job
dispatcher which in its turn notify the job monitor about the completion of the job and sends the results back
to the local agent of the cloud users.
2.3. The cloud service provider’s part
This part contains the physical machine (central processing unit (CPU), random access memory
(RAM), storage, and bandwidth) and the virtual machines. Each physical machine may contain one or more
VMs. For each resource, there is an agent that is responsible for receiving the job description from the CSB
and notifying the job monitor with parodic messages about the status of the job. It is used to provision and
monitors the work of the cloud providers during the job running.
3. EXPERIMENTS AND RESULTS
In this part, two experiments were created and evaluated using a CloudSim [27]. A CloudSim is
employed to evaluate the fulfilment of the suggested DCM system. CloudSim is one of the primary
simulation environments for the cloud computing model. Both experiments have the same settings. The first
experiment was divided into two parts. The settings include 300 cloudlets (jobs) from 3 cloud users
(100 each), with one machine (one physical machine and three virtual machines) as cloud providers. The
service broker is designed to accept up to 250 cloudlets and reject all the requests above that. The simulation
period of 24 hours in the scheduling interval is 300 seconds. Tables 1, 2, and 3 show the features of the
physical machine, the VMs, and the cloudlets, respectively.
The difference between the two parts is that in the first one, no paid services for medium or high
priority were done. In this situation, the local agents assigned for each of the three requesters will count the
priority for each job as equal (low priority for the three users. In the next part, the local agent at one of the
requesters marked the cloudlets as a high or medium priority whereas the other two requesters with no
marking priority. Both experiments intended to direct all the cloudlets to the cloud broker simultaneously to
examine the time needed to process all the jobs together. Also, it is planned to examine the number of
rejected jobs due to the huge number of requests.
Table 1. Virtual machines features
Feature Quantity/Size
Quantity of VMS 3
VMs categories 4
Million instructions per second of CPU 1000
RAM size 1024
Bandwidth 100 Mbit/s
Storage size 5 GB
Table 2. Physical machine features
Number of machines 1
VMs categories 4
Million instructions per second of CPU 1000
Number of PEs 2
RAM size 1024
Bandwidth 1 Gbit/s
Storage size 10 GB
Table 3. Cloud features
Number of Jobs 300
Output size 400
File size 400
Job length 800

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The two parts of the simulations were compared and evaluated on several aspects such as the time
needed to finish the cloudlets and the number of cloudlets rejected for every user. First Part: 300 cloudlets
were sent to the service broker from three users. There is no priority at this stage. The broker received the
cloudlets and processed them as first in first out jobs. The first 100 cloudlets coming from the first user were
processed without any rejected jobs. The same for the second user. But for the third user, the broker rejected
the last 50 jobs as they reached the boundaries of the accepted requests (as in its policy). The third user had to
send back the rest of the jobs later for processing. Figures 7 and 8 show the processing time and the rejected
jobs, respectively, for the first part of the experiment.
Second part: again 300 cloudlets were directed to the broker. But at this stage, the local agent
marked the jobs for user number 3 as paid service-high priority service. The other users remain a low priority
(normal priority). Figures 7 and 8 show the processing time and the rejected jobs, respectively, for the second
part of the test.
Figure 7. Processing time for both parts-experiment 1
Figure 8. Rejected Jobs for both parts-experiment 1
The results show that user 3 has taken advantage of the paid service, as all of user 3’s tasks were
processed, and the time needed for the processing was very short compared to the first part. Also, there are no
rejected tasks for this user. So, by using this way, it has given the guarantee of completion of all the jobs in a
short time. The other results for users 1 and 2 show that user 1 still has no rejected jobs while user 2 has 45
rejected jobs, as the broker processed their cloudlets according to the first in first out mechanism. But the
overall processing time for the 300 cloudlets is slightly less than the first part. Also, the number of rejected
jobs in the second part is slightly less than in the first one.
To validate the previous results, another scenario was applied. The settings this time include
300 cloudlets (jobs) from 4 cloud users (75 each), with three virtual machines and one physical machine as a
cloud provider. The broker is designed to handle up to 200 cloudlets and rejects all the requests above that.
The same characteristics of the physical machine, the VMs, and the cloudlets, as in the first experiment as
shown in Tables 1, 2, and 3 respectively.
0
10000
20000
30000
1 2 3
Processing
Time
(ms)
User Number
Processing Time
Processng Time- Exp 1 Processng Time- Exp 2
0
20
40
60
1 2 3
0 0
50
0
45
0
Number
of
Rejected
Jobs
User Number
Rejected Jobs
Rejected Jobs- Exp 1 Rejected Jobs- Exp 2

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Again, the experiment was divided into two parts. The two parts of the experiments were compared
and evaluated on several aspects as the time needed to finish the cloudlets and the number of cloudlets
rejected for each user. First part: 300 cloudlets were sent to the broker from 4 users. No priority at this stage.
The broker received the cloudlets and processed them like a FIFO job. The first 75 cloudlets coming from the
first user were processed without any rejected jobs. The same for the second user. But for the third user, the
broker rejected the last 25 jobs as they reached the boundaries of the accepted jobs (as in its policy). The
cloudlets for the last user were rejected. The third and the fourth users had to resend the rest of their jobs later
for processing. Figures 9 and 10 show the processing time and the rejected jobs, respectively, for the first part
of the experiment.
Second part: again, 300 cloudlets were transmitted to the broker. In this round, the local agent
marked the jobs for user number 3 as a medium priority (paid service), and the local agent for user 4 marked
the jobs as high priority. The rest of the users remain a low priority. Figures 9 and 10 show the processing
time and the rejected jobs, respectively, for the second part of the experiment.
The results show that users 3 and 4 have taken advantage of the priority service when all of the users
3’s jobs and ‘4’s jobs were processed, and the time needed for the processing was very short compared to the
first part. Also, there are no rejected jobs for those users. The other results for users 1 and 2 show that user 1
still has no rejected jobs whilst user 2 has 73 rejected tasks, as the broker processed their cloudlets according
to the first in first out mechanism. But the overall processing time for the 300 cloudlets is slightly bigger than
the first part. Also, it is noted that the rejected jobs are fewer than the ones in part 1 of the second
experiment.
Figure 9. Processing time for both parts-experiment 2
Figure 10. Rejected jobs for both parts-experiment 2
4. CONCLUSION
This paper presents a DCM system which can handle a huge amount of cloud requesters while
considering the required quality for the clients’ requirements as regulated by the SLA. This system is being
applied by the CSB and local agents attached to the cloud service requesters and providers. The proposed
DCM system is assessed using the CloudSim tool. The results indicate that using the DCM system improves
0
10000
20000
30000
1 2 3 4
Processing
Time
(ms)
User Number
Processing Time
Processng Time- Exp 1 Processng Time- Exp 2
0
50
100
1 2 3 4
0 0
25
75
0
73
0 0
Processing
Time
(ms)
User Number
Rejected Jobs
Rejected Jobs- Exp 1 Rejected Jobs- Exp 2

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the degree of customers’ QoS conditions during resource employment and, at the same time, avoids cloud
SLA violations. Numbers of future works that can be implemented in this field, such as user responsive
(Encoding); where clouds are usually very complex and difficult to encode and enforce the user policy within
the broker decisions.
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883
BIOGRAPHIES OF AUTHORS
Tariq Alwada’n is a senior lecturer in Computer Networks/Cybersecurity at the
School of Computing, Engineering and Digital Technologies at Teesside University, United
Kingdom since July 2020. He holds a PhD in Computer Science from De Montfort University,
UK since 2012. His master’s degree is in Computer and Information Networks from University
of Essex, UK in 2007. His BS is in Computer Engineering from Al Balqa Applied University,
Jordan in 2005. His main research interests are cloud computing, fog computing, grid
computing, iot, big data mobility in distributed systems, and security. He is certified and
licensed as a CCNA instructor from Cisco Network Academy before joining Teesside
University, He worked as a lecturer at the Higher Colleges of Technology, UAE between 2019
and 2020. He also worked as an Associate Professor of Computer Science at WISE University,
Jordan between 2012 and 2019. He can be contacted at email: t.alwadan@tees.ac.uk.
Abdel-Karim Al-Tamimi is a senior lecturer in the Department of Computing at
Sheffield-Hallam University and a member of the Applied Software Engineering Research
Group. Received his M.Sc. and Ph.D. degrees in Computer Engineering from Washington
University in St. Louis (ranked 50th by The World University Ranking) under the advisement
of Prof. Raj Jain in 2007 and 2010, respectively. His research interests include machine
learning, multimedia systems, and cyber security. He can be contacted at email:
a.al-tamimi@shu.ac.uk.
Adel Hamdan Mohammad is an Associate Professor in Computer Science
Department, The World Islamic Sciences and Education University, Amman-Jordan. Born in
Jordan in 1973. He received His B.Sc. from Philadelphia University in 1998. Then Master’s
degree in Computer Information Systems from Arab Academy for Banking and Financial
Science in 2005, and Ph.D. in Computer Information Systems in 2009 from Arab Academy for
Banking and Financial Science. He Works in the Applied Science Private University and The
World Islamic Sciences and Education University. His research interest is machine learning,
artificial intelligence, and cybersecurity. Certified CCNA, CEH. Also certified and licensed to
teach and issue certificates in the following courses from Cisco Network Academy:
Cybersecurity Essentials, IoT, NDG Linux, Introduction to Cybersecurity and others. He can
be contacted at email: adel.hamdan@wise.edu.jo.
Maher Salem is a lecturer in the department of informatics at King’s College
London, UK. He received his M.Sc. from Duisburg-Essen University, and the Ph.D. from
Kassel University, both from Germany. He is also the department Career Officer at King's
College. Dr. Salem has more than 20 years of industrial and academic experience in
cybersecurity. His research interests include intrusion detection system, threat intelligence,
machine learning, and cybersecurity in education. He can be contacted at email:
maher.salem@kcl.ac.uk.
Yar Muhammad is working as a Senior Lecturer (Assistant Professor) with
Teesside University, United Kingdom, where he is also a part of the Centre for Digital
Innovation. He received a Ph.D. degree in information communication technology (ICT) from
the Tallinn University of Technology, Estonia, in 2015 and a master’s degree in computer
engineering from Mid Sweden University, Sweden, in 2009. He taught at the University of
Tartu, Estonia. He received a Young Investigator Award, which was awarded by Springer and
IFMBE at 16th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics and
Medicinteknikdagarna 2014, Sweden; and he was runner-up for the Best Paper Award in the
26th ISSC, Ireland, in 2015. He can be contacted at email: Yar.Muhammad@tees.ac.uk.

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