A multi objective hybrid aco-pso optimization algorithm for virtual machine placement in cloud computing

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 474
A MULTI-OBJECTIVE HYBRID ACO-PSO OPTIMIZATION
ALGORITHM FOR VIRTUAL MACHINE PLACEMENT IN CLOUD
COMPUTING
B. Benita Jacinth Suseela1
, V. Jeyakrishnan2
1
PG Student, Department of Computer Science and Engineering, Karunya University, Tamilnadu, India
2
Assistant Professor, Department of Computer Science and Engineering, Karunya University, Tamilnadu, India
Abstract
Cloud computing is made possible through virtualization technology which makes virtual machines (VMs) to be placed in physical
servers. The process of assigning VMs to servers is called virtual machine placement. VM placement in cloud is done by focusing on
many objectives like VM allocation time, energy consumption, SLA violation, utilization of resources, etc. In this paper, a multi-
objective hybrid ACO-PSO optimization algorithm is proposed for minimizing resource wastage, minimizing power consumption and
for load balancing in physical servers. Simulation results show that the proposed algorithm reduces resource wastage and power
consumption and also provides load balancing in servers when compared to the existing multi-objective ant colony system algorithm.
Keywords: Cloud computing, Multi-objective optimization, Virtual machine and Virtual machine placement
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1. INTRODUCTION
Cloud computing is a paradigm for computing and delivering
services over the internet [1]. Three major service models of
cloud computing are Software as a Service (SaaS), Platform as
a Service (PaaS) and Infrastructure as a Service (IaaS). In
cloud infrastructure, virtualization enables hardware resources
on one or more servers to be divided into multiple execution
environments called virtual machines (VMs) through
hardware or software partitioning. In virtualization, placement
of VMs in physical servers is one of the research problems in
cloud.
Several research works [2-5] addressed VM placement
problem by considering different objectives like reducing VM
allocation time, reducing number of physical servers needed,
reducing resource wastage and improving power efficiency.
Some research works did VM placement by considering one
of the above objectives (i.e., single objective) or more than
one objective which is mentioned above (i.e., multiple
objective). Static Server Allocation Problem (SSAP) algorithm
[2], Static Server Allocation Problem with variable workload
(SSAPv) algorithm [2], Dynamic Server Allocation Problem
(DSAP) algorithm [2] and Novel Vector Based Approach for
Static VM Placement algorithm [4] are single objective
algorithms for reducing the number of servers required. Multi-
objective Ant Colony Optimization (ACO) algorithm [3] is a
multi-objective algorithm for reducing resource wastage
(CPU, memory, bandwidth), power wastage and Service Level
Agreement (SLA) violation. VM Scheduler algorithm [5] is
also a multi-objective algorithm for reducing VM allocation
time and improving resource utilization. Yongqiang et al. [6]
proposed a multi-objective algorithm named Virtual Machine
Placement Ant Colony System (VMPACS) algorithm for
improving power efficiency and resource utilization in each
server. In this paper, hybrid ACO-PSO optimization algorithm
is proposed for reducing resource wastage and power
consumption and also to provide fault tolerance through load
balancing. The performance of the proposed algorithm is
compared with the VMPACS algorithm.
This paper is organized as follows. Section 2 describes
proposed hybrid algorithm for VM placement in cloud.
Section 3 explains the results. In Section 4, the conclusion is
given.
2. PROPOSED HYBRID ACO-PSO ALGORITHM
The goal of the proposed hybrid ACO-PSO algorithm is to
find VM placement solutions which will reduce total resource
wastage (i.e., CPU and memory wastage) and power
consumption in physical server and to provide fault tolerance
through load balancing. VM placement solution tells us which
VM has to be placed in which server.
2.1 Description
In proposed hybrid ACO-PSO algorithm, hybridization is
done sequentially [7] i.e., the VM placement solutions
obtained by ACO algorithm are given as input to PSO
algorithm. ACO algorithm finds VM placement solutions by
considering resource wastage and power consumption in each
server and PSO algorithm finds VM placement solution when

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considering fault tolerance through load balancing in each
server. ACO algorithm and PSO algorithm are used as local
search algorithm and global search algorithm respectively in
this proposed hybrid algorithm. Two phases in the proposed
hybrid ACO-PSO algorithm are initialization phase and
iteration phase. In initialization phase, all parameters in ACO
and PSO algorithm are set. In iteration phase, each ant
receives all VM requests and assigns physical server for each
VM according to its requirements. Power consumption (P) of
each server is calculated according to equation (1).
P = y * (Pbusy
- Pidle
) (1)
Where y is a binary variable which indicates whether the
server is in use or not Pbusy
and Pidle
are power consumption
values when the server is fully utilized and idle respectively.
The resource wastage (W) in each server is calculated by the
equation given in (2).
W = y * ( | ( Tpi - ( Σi=1 to n (xi . Rpi) ) ) - ( Tmi - ( Σi=1 to n (xi .
Rmi) )) | ) / ( Σi=1 to n (xi . Rpi) + Σi=1 to n (xi . Rmi) ) (2)
Where n is the number of VMs and y is a binary variable
which indicates whether the server is in use or not. Tpi and Tmi
are threshold of CPU utilization and memory utilization in
each server respectively. Rpi and Rmi are CPU and memory
required by each VM respectively. xi is a binary variable
which indicates if VM i is assigned to this server.
The VM placement solutions obtained by ACO algorithm are
updated in pheromone-particle table and given as input to PSO
algorithm. PSO algorithm generates particles (i.e., VM
placement solutions) and fitness of each particle (i.e.,
calculation of load in each server) is calculated by equation
(3).
f = Σj=1 to k uj (3)
Where uj denotes CPU utilization of VM j in a server and k is
the number of VMs placed in that particular server.
After calculating fitness of each particle (i.e., VM placement
solution), best position of each particle is found. Both
particle’s position Xi
t+1
and particle’ velocity Vi
t+1
is found
using the formula given in (4) and (5) respectively.
Vi
t+1
= Vi
t
+ (Xlbesti(t) - Xi
t
) + (Xgbest(t) - Xi
t
) (4)
Where Vi
t
is velocity of particle i and Xi
t
is position of particle
i. Xlbesti(t) denotes velocity of local best particle i and Xgbest(t)
denotes velocity of global best particle.
Xi
t+1
= Xi
t
+ Vi
t+1
(5)
where Xi
t
is position of particle i and Vi
t
is velocity of particle
i.
After the calculation of particle’s velocity and position, VM
placement solutions obtained by PSO algorithm are updated in
pheromone-particle table. The above process is repeated until
a maximum number of iterations.
2.2 Steps in Hybrid ACO-PSO Optimization
Algorithm
Step 1: Collecting the inputs.
 Set of servers with its resource capacity.
 Set of VMs with its resource requirements.
Step 2: Initialization.
 Particle population size, number of ants and maximum
number of iterations MAX_ITER are initialized.
Step 3: Generate initial population by VM placement solutions
based on power consumption and resource wastage of each
server.
Step 4: Update particle-pheromone table with solutions
obtained from ACO.
Step 5: Generate new particles.
Step 6: Calculate the fitness of all particles and obtain local
best position of every particle.
Step 7: Update the particle’s velocity.
Step 8: Update the particle’s position.
Step 9: Update particle-pheromone table with solutions
obtained from PSO.
Step 10: Find the global best solution.
Step 11: Go to step 3 if the iteration value is less than
MAX_ITER or go to step 12.
Step 12: Output the global optimal solution for VM placement
3. RESULTS
The proposed algorithm is simulated in cloudsim.The
performance of the proposed hybrid ACO-PSO optimization
algorithm is compared to multi-objective ant colony system
(ACS) algorithm Yongqiang et al.[7].
Chart -1: Power consumption in each server

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Chart 1 compares the total power consumed in each server
when proposed hybrid ACO-PSO (HACOPSO) algorithm and
Virtual Machine Placement Ant Colony System (VMPACS)
algorithm are used. From the graph, we can understand that
the total power consumption in each server is reduced when
the proposed HACOPSO algorithm is used.
Chart -2: Memory wastage in each server
Chart 2 compares the total memory wastage in each server
when proposed HACOPSO algorithm and VMPACS
algorithm are used. From the graph, we can understand that
the total memory wastage in each server is minimized when
the proposed HACOPSO algorithm is used.
4. CONCLUSIONS
In cloud environment, the placement of VMs in available
servers is one of the research problems. In this paper, a multi-
objective hybrid ACO-PSO algorithm is proposed for VM
placement. The proposed hybrid algorithm gives VM
placement solution which reduces the total resource wastage
and power consumption in servers and provides fault tolerance
through load balancing in servers. By this, the overall server
cost is reduced. In our future work, we will propose a VM
placement algorithm for reducing SLA violation.
REFERENCES
[1]. Q. Zhang, L. Cheng, R. Boutaba, Cloud computing: state-
of-the-art and research challenges, J. Internet Services Appl. 1
(1), 2010, pp. 7–18.
[2]. M. Bichler, T. Setzer, B. Speitkamp, Capacity planning
for virtualized servers, in: Workshop on Information
Technologies and Systems (WITS), 2006.
[3]. Fei MA, Feng LIU, Zhen LIU, Multi-objective
Optimization for Initial Virtual Machine Placement in Cloud
Data Center, in: Journal of Information & Computational
Science, 2012, pp. 5029–5038.
[4]. Mayank Mishra, Anirudha Sahoo, On Theory of VM
Placement: Anomalies in Existing Methodologies and Their
Mitigation Using a Novel Vector Based Approach, in: IEEE
International Conference on Cloud Computing (CLOUD),
2011, pp. 275–282.
[5]. Sameer Kumar Mandal, Pabitra Mohan Khilar, Efficient
Virtual Machine Placement for On-Demand Access to
Infrastructure Resources in Cloud Computing, in: International
Journal of Computer Applications, 2013, pp. 6–11.
[6]. Yongqiang Gao, Haibing Guan, Zhengwei Qi, Yang Hou,
Liang Liu, “A multi-objective ant colony system algorithm for
virtual machine placement in cloud computing”, Journal of
Computer and System Science 79(8), 2013,pp. 1230-1242.
[7]. Cheng-Lung Huang, Wen-Chen Huang, Hung-Yi Chang,
Yi-Chun Yeh, Cheng-Yi Tsai, “Hybridization strategies for
continuous ant colony optimization and particle swarm
optimization applied to data clustering”, Applied Soft
Computing 13(9), 2013, pp. 3864–3872.

A multi objective hybrid aco-pso optimization algorithm for virtual machine placement in cloud computing

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