Mobile Agents-Based Framework for Dynamic Resource Allocation in
Cloud Computing
Safia Rabaaoui
1 a
, H
ela Hachicha
and Ezzeddine Zagrouba
1 b
University of Tunis El Manar, Higher Institute of Computer Science, Laboratory of Informatics, Modeling and Information
and Knowledge Processing (LIMTIC), 2 Rue Abou Rayhane Bayrouni, Ariana 2080, Tunisia
College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Cloud Computing, Multi Agent-System, Mobile Agent, Dynamic Resource Allocation, Cost, Makespan, Task
Nowadays, cloud computing is becoming the more popular technology for various companies and consumers,
who benefit from its increased efficiency, cost optimization, data security, unlimited storage capacity, etc.
One of the biggest challenges of cloud computing is resource allocation. Its efficiency directly influences the
performance of the whole cloud environment. Finding an effective method to address these critical issues
and increase cloud performance was necessary. This paper proposes a mobile agents-based framework for
dynamic resource allocation in cloud computing to minimize the cost of virtual machines and the makespan.
Furthermore, its impact on the best response time and task rejection rate has been studied. The simulation
shows that our method gave better results than the former ones.
The cloud is a term used to refer to a network of re-
mote servers that are accessed over the Internet to
store, manage, and process data instead of relying on a
local server or personal computer (Yahya et al., 2020).
It provides on-demand access to a shared pool of com-
puting resources that can be rapidly provisioned and
scaled to meet users’ needs, including storage, pro-
cessing power, and applications. The cloud enables
users to store and access data and applications from
anywhere with an internet connection, eliminating the
need for local storage and computing infrastructure. It
offers flexibility, scalability, and cost-effectiveness, as
users can pay for the resources they use rather than in-
vest in and manage their own hardware and software
infrastructure. One of the main challenges in cloud
computing is resource allocation due to the dynamic
nature of resources (Kumar et al., 2018). In fact,
with companies and consumers expanding their re-
quirements, the need for efficient resource allocation
is also emerging. Also, the cost of cloud resources
represents a serious concern among many companies
and consumers. This is why cloud resource alloca-
tion is a topic of discussion, and this is to meet the
current demand in the best response time and reduce
cloud services costs. This issue is a challenge in
cloud computing systems. Computing services must
be highly dependable, scalable, and autonomous to
support ubiquitous access, dynamic discovery, and
composability (Buyya et al., 2008). Resource allo-
cation in cloud computing faces challenges, includ-
ing cost efficiency, response time, computational per-
formance, and scheduling tasks (Belgacem, 2022).
Users of cloud computing services target to accom-
plish tasks with the lowest costs possible. Research
in multi-agent systems (MAS) and mobile agents has
evolved considerably, and several distributed systems
have been deployed using these technologies. Indeed,
MAS is based on autonomous and intelligent agents
sharing a typical environment. These agents cooper-
ate and interact with each other to achieve a global
goal. While there are differences between cloud com-
puting and multi-agent systems, they are both tech-
nologies designed for distributed models. Thanks to
their advantages and features, many issues in differ-
ent fields can be solved by integrating or combining
cloud and multi-agent systems, as in (Qasim et al.,
2020). Also, in (Bei et al., 2022), the authors pro-
posed a fair and efficient multi-resource allocation for
cloud computing. In addition, research has been per-
Rabaaoui, S., Hachicha, H. and Zagrouba, E.
Mobile Agents-Based Framework for Dynamic Resource Allocation in Cloud Computing.
DOI: 10.5220/0012390200003636
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) - Volume 3, pages 766-773
ISBN: 978-989-758-680-4; ISSN: 2184-433X
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
formed in recent years to provide architectures for us-
ing the cloud in MAS, such as (Venkateshwaran et al.,
2015), which presented a framework for agent ne-
gotiation while using the resources provided by the
cloud environment. Other research focuses on using
agent technologies to address cloud computing diffi-
culties. Among these, we mention the work of (Ma-
havidyalaya and Nadu, 2021), (Barkat et al., 2021),
and (Yahya et al., 2020), who recommended employ-
ing MAS to address cloud challenges. The authors
of (FAREH, 2015) presented an architecture based on
self-organizing agents to deal with the difficulty of
cloud service composition. Current researchers han-
dle these issues dependently without achieving an op-
timal solution. Therefore, this work focuses on dy-
namic resource scheduling for efficient resource allo-
cation. Synchronously, we must maintain customer
service quality by minimizing the makespan and cost
of virtual machines. The contributions of our research
Create a dynamic resource allocation model to in-
crease resource utilization and user satisfaction by
minimizing task rejection rate.
Propose a new Dynamic Resource Allocation ap-
proach with Mobile Agents DRAMA to minimize
the cost and makespan.
This paper is structured as follows. Section 2 is a list
of related works. Section 3 shows the problem state-
ment and formulation. Section 4 describes our pro-
posed solution. Experiment results and the implemen-
tation of a proposed prototype are reported in Section
5. Finally, Section 6 concludes the paper by identify-
ing our plans.
This section will discuss some of the most relevant
frameworks that focus on using multi-agent systems
and mobile agents for resource allocation in cloud
computing. In (Soltane et al., 2018), authors have
proposed a cloud architecture based on a multi-agent
system exhibiting a self-adaptive behavior to address
dynamic resource allocation DRA. The principal fo-
cus of this work is to enhance energy consumption
while satisfying the quality of service QoS demanded
by users. This architecture consists of four agents: an
analyzer agent who identifies the resources and ser-
vices required by users and builds specific queries.
The scheduling agent is responsible for allocating
resources users need and making the final decision
about resource allocation. The controller agents track
the status of resources in the data center. The coordi-
nator agent supervises the whole process.
Wang et al.(Wang et al., 2016) have defined a de-
centralized multi-agent-based Virtual Machine (VM)
allocation approach. The approach aims to allocate
VMs to Physical Machines (PMs) while minimizing
system energy costs. This approach allows dispatch-
ing a cooperative agent to each PM to assist the PM in
managing resources. Another solution based on agent
technology, called low-level resource distribution, is
proposed by (Bajo et al., 2016). This approach allows
the distribution of computational resources through-
out the entire cloud computing infrastructure, con-
sidering its complexity and associated computational
costs. In this system, agents are distributed over the
infrastructure. Each physical server in the cloud envi-
ronment contains a set of stationary agents in charge
of monitoring and making decisions that involve as-
signing or removing nodes for a particular service.
Each service offered to the cloud users is associated
with two agents, one for monitoring and the other
for control; both are responsible for ensuring compli-
ance with the previously established SLA agreement.
Other agents also ensure the proper operation of the
cloud computing system. On the other hand, some ap-
proaches have been proposed using mobile agents. In
(Singh et al., 2017), a new mechanism was proposed
that deploys various intelligent agents to reduce the
cost of virtual machines and resource allocation com-
plexity. This system defines four stationary agents
and one mobile agent, which searches the resources
from the available resource instances of a current data
center. The mobile agents can manage resource allo-
Further, Aarti Singh et al. (Singh and Malhotra,
2015) proposed using mobile agents for resource allo-
cation in cloud computing, focusing on cost optimiza-
tion. The end users send the resource request to the
cloud data center, where all the resources are avail-
able. Every cloud is associated with a cloud Mobile
Agent (MAc). Every MAc is responsible for all in-
formation on resources and their status, whether free
or allocated. Initially, a service request arrives at an
MAc and checks the available free resources to de-
cide whether the request can be served. After that, the
resource manager agent (RMA) will determine how
it would be allocated, i.e., which technique should be
applied to resources so that they will be adequately
distributed and the cost of VMs minimized. Finally,
resources are distributed to the user.
Belgacem Ali et al. (Belgacem et al., 2020) focus
on dynamic resource allocation. The authors present
a multi-objective search algorithm called the Spac-
ing Multi-Objective Antlion algorithm (S-MOAL) to
Mobile Agents-Based Framework for Dynamic Resource Allocation in Cloud Computing
minimize the makespan and the cost of virtual ma-
chines. However, they did not consider the system’s
energy consumption and fault tolerance issues. In
the work (Abdullah and Surputheen, 2022), the au-
thors present the Cooperative Agents Dynamic Re-
source Allocation and Monitoring in Cloud Comput-
ing CADRAM system. They include more than one
agent to manage and observe resources provided by
the service provider. The solution increases resource
utilization and decreases power consumption while
avoiding SLA violations. He presented an algorithm
to select a virtual machine called the node failure dis-
covery. Hence, VMs are allocated to the user based
on the type of the job.
This research focuses on the dynamic resource alloca-
tion in Infrastructure as a Service (IaaS). In the IaaS
model, computing resources are provisioned and de-
livered over the Internet in a virtualized environment.
IaaS provides users with virtualized infrastructure
components such as virtual machines, storage, and
networking capabilities. The cloud resources provider
makes virtual machines available to customers when
it sends requests to determine their resource require-
ments via the graphical user interface GUI. Each vir-
tual machine is configured with its own processors
and memory resources, which gives an overview of
a resource pool. These resources are dynamically re-
served and released. The first aim of our research
is to develop a dynamic resource allocation system
that acts as an intermediary between cloud providers
and users. This system allows us to connect these
two partners: cloud users who want to choose the ap-
propriate resources that meet their needs and cloud
providers who seek to maximize their benefits and al-
locate their resources efficiently. We decided to use
mobile agents and multi-agent systems that are widely
used to design and develop complex and distributed
systems. The cloud computing system has j Datacen-
DC = {DC
} (1)
Each data center comprises a set of physical nodes
PN = {PN
} (2)
Where p signified the number of physical nodes
in the data center, and many virtual machines (VMs)
reside on each physical node. Each VM is a resource
modeled as a set:
V M = {V M
,V M
,...,V M
} (3)
Where m signified the number of virtual machines
running on PN at a given time.
To handle this problem, we consider that the input
is a set of tasks and a set of VMs, where:
T = {T
} (4)
Represent a set of tasks. Output obtaining a better
mapping of T
to V M
, (T
,V M
) in order to reduce
the cost and the makespan. We consider a parallel-
machine scheduling problem with n Tasks and m ma-
chines: The makespan is the time taken to complete
all the Tasks when optimally allocated across the ma-
chines. The equation can be represented as:
M(t) = max(m
) (5)
Where m
represent the completion times
of each machine.
m(n) =
) (6)
Where p
represents the processing time of each Task
n assigned to machine m. The cost of VM utilization
is denoted by C is the sum prices of running all the
tasks on the VM, as shown in (7).
C(t) =
(t) (7)
(t) = Cost
The proposed dynamic resource allocation with mo-
bile agents (DRAMA) model permits to handle with
the main issues: minimizing the makespan, the cost,
and the rejection rate of tasks. As depicted in Fig. 1,
our architecture is structured into three layers, each
with specific roles and responsibilities within the ar-
1. User Layer: it provides an interface to access the
cloud services through which users specify their
resource allocation needs.
2. Resource Allocation Layer represents an inter-
layer between the upper layer (users) and the
lower layer (data center infrastructure).
3. Data Center Infrastructure Layer: it provides re-
sources as services. It consists of a physical layer
(physical machines, hosts) and a virtual layer (Vir-
tual Machines, VMs).
ICAART 2024 - 16th International Conference on Agents and Artificial Intelligence
Figure 1: The Architecture’s Layers.
The proposed architecture shown in Fig. 2 in-
volves a set of stationary and mobile agents interact-
ing and collaborating to achieve a common goal. In
this architecture, we have integrated the concept of
cloud providers. Within the same provider, it is pos-
sible to have many data centers which are connected
through a network.
For this reason, we have defined a new class of
mobile agents (Data Center Management Agents) that
has a mission to migrate from one data center to an-
other related to one cloud provider. Also, we have as-
signed each user request to one mobile agent (Alloca-
tion Agent) that migrates from one provider to another
to make allocation decisions and define the allocation
plan. In the following, we describe each agent’s roles
and internal architecture in our architecture.
Request Agent RA: is a stationary agent that acts
as an intermediary between the user and the system.
RA is responsible for retrieving the data sent from the
user interface and formulating the user’s request. Af-
ter receiving the requests, RA organizes the requests
(tasks) based on a task scheduling algorithm to use the
available resources correctly. Every task is scheduled
by a scheduling algorithm in such a way that each task
is forwarded to the allocation agent. It is also account-
able for building specific queries (Request Q). The re-
quest Q delivered by the request agent is composed of
three elements, as follows:
Q = (R,Cp,Qos) (9)
R = (r
) (10)
Equation (10) represents resources and services the
user needs (such as data storage and virtual ma-
Cp = (cp
) (11)
Cp is the capacity corresponding to each resource;
Qos = (Qos
) (12)
Qos represents the Qos for each resource. We
represent the combination of resources and their
corresponding requested capacities and Qos to build
the suitable V M
r1 cp1 Qos1
r2 cp2 Qos2
r3 cp3 Qos3
The Request Agent uses the Task Scheduling al-
gorithm 1 to assign the tasks to allocation agents. The
priorities for each task are assigned based on the job
size and inter-arrival time. Then, the tasks are con-
verted into priority-based tasks represented by:
T Q = (T Q
,T Q
,...,T Q
) (14)
Then, they will search for the priority queue.
Input : Tasks T={t1,t2,,,,,,,tn};
Output : connections by the Allocation Agent;
for all t1,t2,,,,,tn in Task scheduler do
Consider for all Task length Sx;
if the current task length = next task length
Sort the request according to their
arrival time At;
Add Task T to Qe based on At;
EnQueue Qe ;
Sort the request according to their
EnQueue Qe;
while Qe!= Null do
Create Allocation Agent AA ;
Add Task TQ to Allocation Agent AA;
Algorithm 1: Scheduling Algorithm.
Input : Tasks T={t1,t2,,,,,,,tn};
Output : best offer (available resources ) ;
1. AA migrates to CSA (provider representative)
to select the appropriate resources for the Task;
2. Receives responses from each CSA (sets of
available resources);
3. It filters and selects the best offer;
Algorithm 2: Allocation Agent Algorithm.
Allocation Agent AA: is a mobile agent that aims to
find the best offer appropriate to the user’s request in
the data-center infrastructure. It receives the task and
migrates to all cloud providers to select the appropri-
ate resources for the received request. It participates
Mobile Agents-Based Framework for Dynamic Resource Allocation in Cloud Computing
Figure 2: The Architecture’s Cloud Providers and Agents.
in the system and aims at obtaining maximum bene-
fit for the user (optimization of the cost-performance
ratio, minimization of the cost).
Cloud Supervisor Agent CSA: It represents the
provider in the cloud environment. This agent is re-
sponsible for cooperating with the allocation agents
and assigning the data-center management agent the
mission to find the virtual machine corresponding to
the user’s needs. The cloud supervisor agent offers an
offer (list of resources to be allocated, prices, etc.) to
respond to a given task. To do this, it cooperates with
the Data-Center Management Agent in order to ex-
ploit the resources available in the different data cen-
Input : needed resources;
Output : available resources ;
1. CSA sends the request to DCMA;
2. It receives all available resources;
3. It proposes an offer (list of resources to be
Algorithm 3: Cloud Supervisor Agent Algorithm.
Data Center Management Agent DCMA: It discov-
ers resource capacities on physical nodes and man-
ages virtual machines in DC. This agent has visibility
into all resources in DC from the same provider. It
maintains the current state of physical machines and
tracks resource status. It is responsible for the coor-
dination between the different service agents. When
it receives a Task from the cloud supervisor agent,
it migrates to all service agents to discover the re-
source capabilities of the different virtual machines,
then prepares the best answer with the minimum cost
and transfers it to the Cloud Supervisor Agent.
Input : CPU, RAM, storage, cost
, cost ram,
,Bandwidth, cost
Output : TotalCost;
for all VM1,VM2,,,,,VMm do
= CPU * cost
= ram * cost
= storage *
= Bandwidth *
TotalCost = total
pu+ total
am +
torage+ total
Algorithm 4: TotalCost Algorithm.
Service Agent SA: is responsible for maintaining the
capacity of physical nodes and handling virtual ma-
chines in the data center. It is responsible for dis-
covering the current state of each machine (capac-
ity used, free capacity, state). It is also responsible
for maintaining information on the status of resources
and characteristics. It also has the mission to man-
age resources (allocation, freeing). For each physical
machine exists one Service Agent.
This section presents the performance and result of
the proposed framework throughout the implementa-
tion of three prototypes. We have used the CloudSim
simulator platform (Calheiros et al., 2011) to simu-
late the cloud infrastructure and the JADE platform
to implement our multi-agent system. The cloud data
center is created by using a series of PMs. Each data
center with multiple numbers of hosts and their cor-
responding VMs is initiated. In the first prototype,
ICAART 2024 - 16th International Conference on Agents and Artificial Intelligence
we implement an allocation system without a multi-
agent system. In the second prototype, we implement
our proposed framework (Rabaoui et al., 2021).
In this architecture, we have only one mobile
agent that migrates from one data center to another
without considering the cloud provider. In the third
prototype, we implement our proposed architecture
using two mobile agents: allocation agents that mi-
grate from one cloud provider to another and data cen-
ter management agents that migrate from one service
agent to another within the same cloud provider.
Table 1 provides the cloud sim specification of
the proposed system. Different metrics, such as
makespan, execution time, Refusal Rate and cost are
used to analyze the simulation results. The proposed
approach (DRAMA) improves system efficiency and
is compared with some existing methods in the liter-
ature, including the First-Come First-Served (FCFS)
algorithm and the Preference Based Task Scheduling
(PBTS) algorithm.
Table 1: Simulation Specification.
Parameter Value
The number of Hosts 4
The number of Tasks 30-300
Length of task 1600-3400
Memory 540
Bandwidth 25,00,00
Storage 500 GB
We use our proposed method. At t=0, a set of tasks
arrive simultaneously. Then, jobs are prioritized by
the task scheduling concept employed in our frame-
work. The prioritized tasks are given to the allocation
agents. AA will migrate and send the same received
job to all cloud supervisor agents (CSA1, CSA2, etc.)
listed in its directory. CSA forwards the task to one
of the Data Center Management agents (DCMA) that
it holds.
DCMA migrates to Service Agents (SA) to re-
trieve virtual machine states. The service agent sends
the current status of each virtual machine to the data
center management agent. DCMA compares the re-
sponses from the service agents to select the best so-
lution that meets the user’s requirements and informs
the cloud supervisor agent. The CSA receives the
best-selected response and sends its offer to the al-
location agent.
AA receives the offers of the CSA, filters them,
and sorts them according to the price. It then selects
the best suggestion that meets the user’s needs. It
sends an allocation request to the CSA if it receives
an allocation confirmation and sends the answer to
the RA. The AA gets an error message from the se-
lected CSA if it is not confirmed. The AA returns an
allocation request to the next CSA in its sorted list of
offers. It enters a loop until it receives an allocation
confirmation from a CSA, or it determines its circle
and fails to allocate a resource, in which case it sends
an error message to RA.
Fig.3 shows the makespan according to the num-
ber of tasks. This measurement reveals to us what
time is needed to receive a response from the system.
The simulation result gives a shorter Makespan when
compared with the other two taken algorithms (FCFS
and PBTS).
Figure 3: Comparison of makespan.
The examination of the proposed approach in
terms of cost is presented in Fig. 4. It is apparent
from the comparison of the three scheduling algo-
rithms that the proposed method, DRAMA, decreases
the value of the cost due to the completion of each
task in an appropriate virtual machine that ensures a
minimum price. Therefore, the proposed method al-
lows a better match between the tasks and the virtual
Figure 4: Costs for different numbers of tasks.
A comparative graph shown in Fig. 5 shows our
proposed method’s lesser response time than existing
The examination of the proposed approach in
terms of the Refusal Rate is presented in Fig. 6.
Re f usalRate = Total
Tr j
T sb
Mobile Agents-Based Framework for Dynamic Resource Allocation in Cloud Computing
Figure 5: Comparison of response time.
Where: Total
Tr j
is the total number of tasks re-
jected, and Total
T sb
is the total number of tasks sub-
mitted. It indicates how many tasks are dismissed to
the total number of submitted jobs and how many are
accepted. It shows that our proposed methods have
significantly lower refusal rates, indicating that they
get more tasks than rejected ones.
Figure 6: Refusal Rate.
In this paper, we proposed a Mobile agents-based
framework for a Dynamic Resource Allocation ap-
proach with Improved task scheduling in cloud com-
puting environments. Our system has two classes of
mobile agents and three classes of stationary agents:
mobile agents play a crucial role in choosing the
most suitable offer for user demand. This agent en-
sures a secure exchange and greatly minimizes traf-
fic on the network. The stationary agents manage lo-
cal information. The performance evaluation results
can significantly improve the makespan, the response
time, and service quality (minimizing VM cost). This
method combines the dynamic change of the cloud
system state and the task scheduling to solve various
issues, which makes it effective.
Three experiments were used to evaluate the per-
formance of the proposed method, DRAMA. The first
shows good results regarding the tasks’ makespan,
cost, and rejection rate. However, there is still much
work to be done. For our future work, we plan to
present a formal model of our approach defining the
query for resource allocation and user preferences.
We also plan to describe in detail the scheduling al-
gorithm and give additional experiments to evaluate
other performance aspects and the security for the re-
source allocation.
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