An efficient approach for load balancing using dynamic ab algorithm in cloud computing.

Prepared By:
Pooja Gothi
En No.-140350702014
M.E. Computer Engg.
N.G.I.
Junagadh
Guided By:
Prof. Piyush Gohel
Asst. Professor
C.E. Department
N.G.I.
Junagadh
An Efficient approach for Load
balancing using Dynamic AB
Algorithm in cloud computing.

OutlineOutline
Introduction
Problem Statements and Motivation
Objectives
Introduction of Approach
Existing System
Literature Review
Proposed Work
Implementation Methodology
Conclusion and Future Work
References

IntroductionIntroduction
 Load balancing is a methodology to distribute workload across
multiple computers or a computer cluster, network links, central
processing units, disk drives, or other resources, to achieve
optimal resource utilization, maximize throughput, minimize
response time, and avoid overload.[1]
 Grid computing is aggregation of autonomous resources that are
geographically distributed. The nodes in grid permit sharing and
selection dynamically at runtime.[1]
 Cloud computing refers to a parallel and distributed computing
system consisting of a collection of inter-connected and
virtualized computers that are dynamically provisioned and
presented as one or more unified computing resources based on
service-level agreements (SLA) established through negotiation
between the service provider and consumers. [1,2]

Conti…Conti…
 Ant colony algorithm:
The ants work together in search of new
sources of food and simultaneously use the existing food sources to
shift the food back to the nest.
 It is a random search algorithm. It takes the behavior of real ant
colonies in nature to search the food and connect to each other by
pheromone laid down on path optimization algorithm. [2,6]
 The Artificial bee colony algorithm (ABC) is an optimization
algorithm based on the intelligent foraging behavior of honey bee
swarm and was proposed by Karaboga in 2005 [15].
 This both algorithm is completely inspired by natural foraging
behavior .
4

MotivationMotivation
 Cloud Computing is the fast growing technology, which shares the
resource to achieve consistency and economies of scale similar to a
utility over a network. Resource sharing requires more optimized
algorithm.
 In ACO there exist limitations like slow convergence, tendency to
stagnancy.
 Bee colony algorithm obtain the solution only particular distance,
because bee provides optimal solution based on small path.
 So I wish to explain both algorithm approaches combine and get
more feasible and optimization for dynamic algorithm.

Problem StatementProblem Statement
 There are certain limitation of ant colony and bee colony
algorithm.
 In ACO ant’s pheromone is a locally not globally works
dynamic in everywhere. After initialization the pheromone of
ants by moving through neighbor node of the construction in
path . Bee colony is not properly work for the allocating the path
in collective some wrong information.
 So, to overcome above problems I need to develop new
approach for better optimization in load balancing and
scheduling to using AB algorithm in effective load balancing.

ObjectivesObjectives
 Ant-Bee algorithm is in fact an optimization on Ant-Net
algorithm and tries to improve its performance .
 This algorithm, at the beginning uses forward ants to find a
suitable solution from one node to another and then these are the
bees who update the pheromone on ants collected data.
 It work like as routing table value in pheromone updating , so the
we rollback ant pheromone to starting towards bee.

Cloud environmentCloud environment
 Cloud is a pool of heterogeneous resources. It is a mesh of
huge infrastructure and has no relevance with its name
“Cloud”.
 In order to make efficient use of these resources and ensure
their availability to the end users “Computing” is done based
on certain criteria specified in SLA. Infrastructure in the
Cloud is made available to the user’s On-Demand basis in
pay-as-you-say-manner. [1]
 Computation in cloud is done with the aim to achieve
maximum resource utilization with higher availability at
minimized cost.
8

Cloud platformsCloud platforms
9

IntroductionIntroduction
 What is load balancing ? why these need of
cloud computing?
 Load Balancing is essential for efficient operations in distributed
environments. As Cloud Computing is a greatest platform which
provides storage of data in very lower cost and available for all
time over the internet, that’s why load balancing for the cloud
has become a very interesting and important research area.
 Load balancing helps to attain a high user satisfaction and
resource utilization ratio by ensuring an efficient and fair
allocation of every computing resource.
 Load balancing makes sure that all the processor in the system or
every node in the network does more or less the equal amount of
work at any moment of time.

Conti…Conti…
The goals of load balancing are to:
• Improve the performance
• Maintain system stability
• Build fault tolerance system
• Increase the availability
• Increase the user satisfaction
• Improve the resource utilization ratio

Effective load balancingEffective load balancing
ApproachesApproaches
 There are tow main approach for load balancing named static
load balancing and dynamic load balancing. [2]
1. Static load balancing.
 In this approach of load balancing, we consider static
information of system to choose the least loaded node.
 It performs better in terms of complexity issue but
compromises with the result as decision is made on statically
gathered data.
 It is further classified as Distributed and Centralized
12

Conti…Conti…
2. dynamic load balancing
 This approach provides QOS aware load balancing.
 In this strategy, current system state plays major role while
making decisions. Despite the fact that dynamic load
balancing has higher run rime complexity then static one,
dynamic has better performance report as it considers current
load of system for choosing next datacenter to serve the
request.
 This will surely provide an optimal choice from available
ones for that state of system.
13

Ant Colony optimizationAnt Colony optimization
 Ant colony optimization (ACO) is a population-based
metaheuristic that can be used to find approximate solutions
to difficult optimization problems.[7]
 In ACO, ant find the food and follows the path and put some
chemical substance which called pheromone.
 And remaining Ant follows the one kind of instruction on the
base of pheromone.
 The solution construction process is biased by a pheromone
table as like routing table.
 ACO algorithm can be used to schedule large-scale work
flows with various QOS parameters .

Here in fig we show the naturalHere in fig we show the natural
behavior of antbehavior of ant
15

Dynamic Ant colonyDynamic Ant colony
OptimizationOptimization
 ACO can be used in many ways , but limitation like slow
conversion and poor performance.
 There is only one kind of pheromone in ACO and the path
and path weight is stable, and cannot fit for dynamic load
balancing.
 Dynamically here Ant follows the instruction of Pheromone
but not finding the best laid path for searching thus dynamic
ACO works behind on the find best path or shortest path to
finding the source.

Bee Colony optimizationBee Colony optimization
 Bee colony optimization (BCO) is a swarm used metaheuristic
algorithm.[13]
 This algorithm simulates the foraging behavior of honey bees. This
algorithm has three phases. There are employee bees, onlooker bees
and scout bees.
 Scout bee: it is responsible for finding new food. The new nectar
source.
 Onlooker bee : It gets the information of food sources from the
employed bees in the hive and select one of the food source to gathers
the nectar.
 Employee bee: It stay on a food source and provides the neighborhood
of the source in its memory
 The algorithm has a well-balanced exploration and development
ability.

Conti…
Bees life algorithm is an optimization algorithm used for job scheduling. Bees
colony contains single breeding female bee called queen and male known as
Drones.
Bees start with scout bees with initial population first the bees choose
randomly in space. Then, fitness is calculated for bees. The highest fitness is
chosen as “selected bees” and remaining bees are workers. The selected bees
alone visit the site by choosing neighbour search.

Why this combined ?
 In traditional many static algorithms are proposed, but all
these algorithms do not produce optimal job scheduling and
load balancing.
 Ant-Bee algorithm is in fact an optimization on Ant-Net
algorithm and tries to improve its performance .
 The AB algorithm is a combination of two dynamic
algorithms, Ant Colony Optimization and Bees Life algorithm.
here in path finding the ant pheromone is not rollback then we
started bee here to continuing the path finding by the
pheromone updating.
19
ANT COLONY
ALGO.
BEE COLONY
ALGO.
AB
ALGORITHM

Existing system
 In existing system researchers have work on Ant colony
algo and Bee colony algorithms used for load balancing
in cloud computing.
 Ant algo is give better work in cloud using load
balancing but it’s not attempt dynamic job to pheromone
value and bee is not used load balancing to another
approach.
 So , now more effective load balancing technique to
consider both Ant & Bee is AB algo to improve load
balancing strategy.
20

Paper Method / Algorithm Advantage Drawbacks
Ant colony optimization
for effective load
balancing in cloud
computing.[2]
(IJETTCS)
Publication year:2014
In here different kinds of
load balancing technique
is used.
Provide dynamic
and effective load
balancing
Ant can move only one
direction ,it can’t rollback.
Workflow scheduling in
grids : An Ant colony
optimization approaches[6]
Ieee paper
Publication year :2007
Aco algorithm used for
scheduling to balanced
load in grid.
Give dynamic
solution using QOS
parameters
In shortest path ant’s choice
is ignoring it work for
priority.
Grid resource
management by means
of Ant colony
optimization.[8]
Ieee paper
Publication Year :
2006
A simple ant-based
technique is proposed in
for resource management
and task scheduling and
its scalability is validated
Established optical
network for
important
requirement in load
balancing
It is not scalable and can’t
extended process to
incorporate information
about job requirement.
Literature Survey

Conti..Conti..
Paper Method / Algorithm Advantage Drawbacks
A bee colony based
multi-objective load
balancing technique
for cloud computing
environment.[7]
(IJCA)
publication year :
2014
Bee colony algo Bee colony and
genetic combination
based algorithm
provides better
efficiency and
effectively utilize the
resource
Runtime fault
tolerance may become
unavailable and
during heavy load
condition improve
QOS parameters.
Improve performance
of load balancing
using artificial Bee
colony.[9]
(IJCA)
Publication year :
march 2015
Load balancing
technique
And some threshold
aspect.
Improve time
efficiency and reduce
makespan time and
minimize the node of
failure
They do not share the
load among the
available resources.
fail in produce load
balance schedule.

ContiConti……
Paper Method/ Algo Advantages Drawback
Artificial Bee colony
algorithm and it’s
application to
generalized assignment
problem.[20]
Source: Swarm
Intelligence: Focus on
Ant and Particle Swarm
Optimization
Publication year : jan
2014
PSO
NP-hard problem is
presented in detail along
with some comparisons.
Effectively solve
small to medium
size task and also
solve complex
optimize problem
Not work on large
size and tightly
constrained
generalised
assignment problem.
An Ant Colony Based
Load Balancing
Strategy in Cloud
Computing.[13]
Publication year : 2014
Springer paper
load balancing strategy
traditional approaches like
(FCFS), local search
algorithm like Stochastic
Hill Climbing
(SHC),another soft
computing approach
Genetic Algorithm (GA)
Give surety for
QOS approach
Requirement is
fulfill here.
fault tolerance and
different function
variation to calculate
the pheromone value
can be used for
further research
work.

Conti…Conti…
Paper Method/Algo Advantage Drawback
Comparison of Ant
Colony and Bee
Colony Optimization
for Spam Host
Detection.[10]
Ijerd journal
Publication year :Nov-
2012
Compare both algo….. Here give
performance by the set
of classification rule.
Cloud result not give
better exact through
put in any critical
condition
An efficient load
balancing using Bee
foraging technique
with Random stealing.
[11]
(IOSR-JCE)
Publication year :
Mar-Apr
2014
Use random stealing
method.
Virtual machine is
idle. It thus saves the
idle time of the
processing element in
a Virtual machine
It provide energy
aware scheduling
24

Proposed systemProposed system
 In proposed system, the ant colony and bees life algorithm are
combined to improve the effectiveness of load balancing .
 The ants in our proposed algorithm will continuously
originate from the Head node. These ants traverse the width
and length of the network in such a way that they know about
the location of underloaded or overloaded nodes in the
network.
 These Ants along with their traversal will be updating a
pheromone table, which will keep a tab on the resources
utilization by each node.
25

AB algorithm used in systemAB algorithm used in system
26

Conti…Conti…
Ant-Bee algorithm
In this section we are briefly explaining Ant-Bee algorithm:[24]
 A forward ant starts traveling through the network. Whenever this
forward ant reaches a node, if this node is not the destination node, it is
directed toward the destination. These forward ants have the same
priority as data packets.
 When a forward ant reaches the destination, based on the provided
information by her, a backward bee is created then the forward ant is
killed and the new born bee continues the journey.
 This backward bee traverses the forward ant’s travelled route in
reversed direction and on its way updates the pheromone tables and is
finally killed at the starting node (the node which had initiated the
forward ant). It has to be mentioned that backward bees have more
priority than data packets to be able to apply the emergency changes as
rapidly as possible.

Work of Ant Bee colonyWork of Ant Bee colony
 The pheromone evaporation at time t be ρt , where the value
of ρt lies on [0,1] now evaporation of pheromone at time t+1
is suggest one natural and traditional equation is
ρt+1 = α ρt + β(1- ρt) = k ρt + β
Where α , β = constant
k= α – β
0 ≤ α , β ≤ 1
f(ρt ) = ρt +1

Conti…Conti…
 Here for effective load balancing in our existing system ant
optimization use updated pheromone is as follows equation.
 Modified ACO Pheromone Updated Strategy:
 Pheromone is a chemical substance which the ants release on
path while traversing the cloud network.
 The probability of traversing a particular path by ants
depends on the pheromone concentration on the path, which
can be retrieved from foraging, trailing pheromone

Conti…Conti…
 The main aim of the two types of pheromone updating
according to the types of nodes they are currently searching
for.
 The ants after originating from the head node, by default
follow the Foraging pheromone, and in the process, they
update the FP trails according to the formula.
 After coming upon an overloaded node they follow the
Trailing Pheromones and simultaneously update the TP trails
of the path.
 After reaching an underloaded node of the same type they
update the data structure so as to move a particular amount of
data from the overloaded node to under loaded node.
31

Conti…Conti…
 We also proposed the movement of ants in two ways similar
to the classical ACO, which are as follows:
1) Forward movement-
The ants continuously move in the forward direction in the
cloud encounter in overloaded node or under loaded node.
2) Backward movement-
If an ant encounters an overloaded node in its movement
when it has previously encountered an under loaded node
then it will go backward to the under loaded node to check if
the node is still under loaded or not and if it ﬁnds it still under
loaded then it will redistribute the work to the under loaded
node. The vice-versa is also feasible and possible.
32

Conti…Conti…
Foraging Pheromone (FP) :
 While moving from underloaded node to overloaded node, ant
will update FP. Equation for updating FP pheromone is
FP( t+1 ) = ( 1 - βeva )FP(t) + ∆FP.
Where, β eva = Pheromone evaporation rate
FP = Foraging pheromone of the edge before the move
FP( t+1 ) = Foraging pheromone of the edge after the move
∆FP = Change in FP

Conti…Conti…
Trailing Pheromone (TP):
 While moving from overloaded node to underloaded node,
ant will update TP. Equation for updating TP pheromone is
TP( t+1) = ( 1 –βeva )TP(t) + ∆TP
Where, β eva = Pheromone evaporation rate
TP = Trailing pheromone of the edge before the move
TP( t+1 ) = Trailing pheromone of the edge after the move
∆TP = Change in TP

Bee optimization in systemBee optimization in system
 The distinguishing element between Ant-Bee and Ant-Net
algorithms is Ant-Bee’s use of backward bees , different kinds
of bees which are in use in this algorithm are:
1. Dancer bee: whenever this bee reaches a node, after updating
the node’s pheromone table, sets down its traverse time in
related field. The pheromone updating strategy is the same as
backward ants in Ant-Net algorithm.
2. Follower bee: These are considered as naive bees that should
collect information based on dancer bee’s dancing parameters.
They use equation for doing so. Assume that the follower bee
has come from node j to i on its way to destination d.

Conti…Conti…
T= β T_foll+(1- β) T_dan 0 ≤ β ≤ 1
Where T_foll= follower bee’s trip time.
T_dan = selected trip time among dance bee based
β : An impact factor which determines the two first factor’s
effect. If β = 1 , it means the follower bee doesn’t pay any
attention to dancer bees and this algorithm will work the same
as Ant-Net algorithm. In contrast, if the follower bee is
entirely following the dancer bee.

Process…Process…
 Ants attract to pheromone laid from ants in some colony, the
so called ” repulsion ” and “attraction” strategy prevents ants
from different colony in same path.
 This strategy is introduced by varela & sinclire. it used in
multiple way network for virtually.
 General structure of a pheromone table for Multiple Ant-Bee
colony is shown in Table .

Pheromone tablePheromone table
To
From
1 2 ….
…….
N
1
.
.
P11 , M11
.
.
P12 , M12
.
.
……………..
P1n , M1n
.
.
.
.
.
.
.
.
.
.
.
……………
.
.
.
L Pl1 , Ml1 P12 ,
M12 ……………
Pln , Mln
38
L= All the outgoing links
N =number of nodes minus one (the node itself).
Each cell contains values M ij and Pij .
Pij= chance of node i to be selected as the next node for colony K
Mij =represents some of the recorded trip times of dancer bees form
K th colony which have travelled from i to j

Conti…Conti…
 Here the proposed algorithm considers some pheromone value for
selecting optimal VM for load balancing.
 Here VMs are defined based on their loads
OVM – Overloaded node.
UVM – Underloaded node
 Here some information to when random node is overloaded then we
put it in by underloaded to balanced the node.
 When load fairly distributing on the server node but finding a load
is imbalance then to achieving this, define a THRESHOLD
variable which tells how much load should exchange between
nodes.
39

Selection process of nodeSelection process of node
 Now updating the pheromone value then selecting a next
node whenever ant is killed here.
We used two strategy
1) Attraction strategy
2) Repulsion strategy
Attraction strategy: we use α parameter to indicate attraction
of a forward ant from colony K to node I on its way to
destination.
Repulsion strategy: In the proposed algorithm we use β
parameter to show repulsion of forward ants of colony K to j
on its way to destination d.

Conti…Conti…
hbijn
: quantity of pheromone K in the edge
linked to node j on its way to destination d(jd
element in pheromone table of K th colony)
Ni= A set containing all possible outgoing edge
for forward ant.
: Repulsion of forward ants of colony K
i to j on its way to destination d.
: Amount of pheromone from all colonies
(except K itself) in edge linked to node j on its
way toward destination d.
Ni= sum of all possible edges for the forward ant.

AB Algo steps.AB Algo steps.
 AB algo Programming Steps:-
 1. Configure cloud analyst tool in develop environment.
 2. Create data center and host servers into it.
 3. Create Virtual Machines (VM) and allocate resources (HDD,
RAM, CPU, and bandwidth) to it.
 4. Run AB algorithm.
 5. Initialize the threshold value per Virtual Machine (VM) in terms
of resources.
 6. Run modified ACO and Artificial Bee algorithm simultaneously
and output optimal solutions. It is done by initializing pheromone
and bee's population. Then pheromone concentration and bees
fitness is checked simultaneously to get the optimal node.
 7. Select common best optimal solution nodes.
 8. Distribute load on them and achieve load balancing.
 9. Move to step 5.
42

Flow of AB algorithmFlow of AB algorithm
43

start
Initialize pheromone
table for ants value
Declare to node
Load move to node
overloaded
Traverse to
maximum
Tp
Update pheromone
Load
is >
Traverse to
minimum FP
Update pheromone
Move on
next strategy

Bee Colony strategy
Check
both
Attraction strategy Repulsion Strategy
Node are balanced
Stop
End of process

Implementation MethodologyImplementation Methodology
 In cloud environment different users, resources , scheduler
implement by Cloudsim.
 Cloudsim is a well known simulation for cloud computing and
designed to support various simulation tests across the IAAS
,PAAS and SAAS.
 Cloud Sim provides support for modeling and simulation of
virtualized Cloud-based data center environments such as dedicated
management interfaces for VMs, memory, storage, and bandwidth.
So AB algorithm used Cloudsim as a developing tool .

Conclusion and future workConclusion and future work
 We have represented survey of existing Ant and Bee colony
work different dynamic strategy in load balancing.
 It can be extended with scenario having more data center
scattered around different location and trying to improve load
balancing approach according to some different way like used
AB algorithm approaches. In future AB algorithm work
establishing to task scheduling and node allocate on load to
random VM.
 In future work I will show whole system in brief that provide
good solution for effective load balancing.

Paper publication detail.Paper publication detail.
Title of paper: ” An Effective Load Balancing
Approach in Cloud Using Dynamic AB Algorithm ”
Journal name: International Journal of Innovative Research
in Science, Engineering and Technology
Impact Factor : 5.45
 DOI:10.15680/IJIRSET.2015.0408129
Date of publication: 24/8/2015
Issue: Vol. 4, Issue 8, August 2015
48

ReferencesReferences
1. Mayanka Katyal, Atul Mishra , “A Comparative Study of Load Balancing
Algorithms in Cloud Computing Environment ” , International Journal of
Distributed and Cloud Computing Volume 1 Issue 2 December 2013 .
2. Shagufta khan Niresh Sharma , “Ant Colony Optimization for Effective Load
Balancing In Cloud Computing ”, International Journal of Emerging Trends &
Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email:
editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 6, November –
December 2013 .
3. Radha G. Dobale, Prof. R. P. Sonar , “Review of Load Balancing for Distributed
Systems in Cloud ”, International Journal of Advanced Research in Computer
Science and Software Engineering 5(2), February - 2015, pp.
4. Rajkumar Somani, Jyotsana Ojha ,” A Hybrid Approach for VM Load Balancing in
Cloud Using CloudSim ” , International Journal of Science, Engineering and
Technology Research (IJSETR), Volume 3, Issue 6, June 2014 .
5. Kumar Nishant, Pratik Sharma, Vishal Krishna, Chhavi Gupta and Kuwar Pratap
Singh,Nitin and Ravi Rastogi , “Load Balancing of Nodes in Cloud Using Ant
Colony Optimization”, 978-0-7695-4682-7/12 $26.00 © 2012 IEEE DOI
10.1109/UKSim.2012.11 2012 14th International Conference on Modelling and
Simulation 978-0-7695-4682-7/12 $26.00 © 2012 IEEE DOI
10.1109/UKSim.2012.11

6. wei –neng chen , jun zhang, IEEE member & yang yu “ workflow
scheduling in grids: An Ant colony optimization Approach” , 4244-1340-
0/07/$25.00 c 2007IEEE.
7. Ashish Soni, Gagan Viswakarma, Yogendra Kumar Jain , “A Bee Colony
based Multi-Objective Load Balancing Technique for Cloud Computing
Environment ” , International Journal of Computer Applications (0975 –
8887) Volume 114 – No. 4, March 2015 .
8. Gustavo Sousa Pavani, Helio Waldman Optical Networking Laboratory
(OptiNet) ,” Grid Resource Management by means of Ant Colony
Optimization” , 1-4244-0425-8/06/$20.00 ©2006 IEEE .
9. Deepika Nee Miku ,Preeti gulia, “Improve Performance of Load
Balancing using Artificial Bee Colony in Grid Computing ” , International
Journal of Computer Applications (0975 – 8887) Volume 86 – No 14,
January 2014
10. R. Sagayam, Mrs. K. Akilandeswari , “Comparison of Ant Colony and
Bee Colony Optimization for Spam Host Detection ” , International
Journal of Engineering Research and Development eISSN : 2278-067X,
pISSN : 2278-800X, www.ijerd.com Volume 4, Issue 8 (November 2012),
PP. 26-32.
11. Ms. Anna Baby , Dr. Joshua Samuel Raj , “An efficient load balancing
using Bee foraging technique with Random stealing ” , IOSR Journal of
Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278- 50

12. Ekta Gupta , Vidya Deshpade ,” A Technique Based on Ant Colony
Optimization for Load Balancing in Cloud Data Center ” , 978-1-4799-
8084-0/14 $31.00 © 2014 IEEE DOI 10.1109/ICIT.2014.54
13. Santanu Dam, Gopa Mandal, Kousik Dasgupta, and Paramartha Dutta ,
“An Ant Colony Based Load Balancing Strategy in Cloud Computing ” ,
M.K. Kundu et al. (eds.), Advanced Computing, Networking and
Informatics - Volume 2, Smart Innovation, Systems and Technologies 28,
DOI: 10.1007/978-3-319-07350-7_45, © Springer International Publishing
Switzerland 2014 .
14. M. A. Mahajan, G. T. Chavan , ” USE OF MULTIPLE ANT COLONY
OPTIMIZATION FOR LOAD BALANCING IN PEER TO PEER
NETWORKS ” , International Journal of Computer Engineering and
Technology (IJCET), ISSN 09766367(Print), ISSN 0976 – 6375(Online)
Volume 4, Issue 3, May – June (2013), © IAEME
15. sokanth Mil , Mongkut Piantanakulchai , ” RANKING NON-
DOMINATED SOLUTIONS IN AUTOMATED HIGHWAY DESIGN
USING THE ANALYTIC NETWORK PROCESS (ANP)” , Proceedings
of the International Symposium on the Analytic Hierarchy Process 2013
51

16. John A. Bullinaria and Khulood AlYahya, ” Artiﬁcial Bee Colony
Training of Neural Networks” , G. Terrazas et al. (eds.), Nature Inspired
Cooperative Strategies for Optimization (NICSO 2013), Studies in
Computational Intelligence 512, DOI: 10.1007/978-3-319-01692-4_15, ©
Springer International Publishing Switzerland 2014.
17. Atul Vikas Lakraa, Dharmendra Kumar Yadavb , “Multi-Objective Tasks
Scheduling Algorithm for Cloud Computing Throughput Optimization ” ,
International Conference on Intelligent Computing, Communication &
Convergence Procedia Computer Science 48 ( 2015 ) 107 – 113 .
18. Liu Youhui, Liu Xinhua, and Li Qi ,” Assembly Sequence Planning Based
on Ant Colony Algorithm ” , Future Communication, Computing, Control
and Management, LNEE 141, pp. 397–404. springerlink.com © Springer-
Verlag Berlin Heidelberg 2012
19. 19. Gurpreet Singh,Parveen Kumar ,” Self-Adaptive Task Distribution for
Load Balancing using HABACO in Cloud ” , International Conference on
Advanced Communication Control and Computing Technologies
(lCACCCTISBN No. 978-1-4799-3914-5/14/$31.00 ©2014 IEEE
20. 20. Adil Baykasoùlu, Lale Ozbakır and Pınar Tapkan , “Artificial Bee
Colony Algorithm and Its Application to Generalized Assignment Problem
” , Source: Swarm Intelligence: Focus on Ant and Particle Swarm
Optimization, Book edited by: Felix T. S. Chan and Manoj Kumar Tiwari,
ISBN 978-3-902613-09-7, pp. 532, December 2007, Itech Education and
Publishing, Vienna, Austria 52

21. Denis Darquennes , “Implementation and Applications of Ant Colony
Algorithms ,” Facultés Universitaires Notre-Dame de la Paix, Namur
Institut d’Informatique Année académique 2004-2005
22. Fairouz Fakhfakh ,Hatem Hadj Kacem , Ahmed Hadj Kacem , “Workflow
Scheduling in Cloud Computing: A survey” , 2014 IEEE 18th International
Enterprise Distributed Object Computing Conference Workshops and
Demonstrations 978-1-4799-5467-4/14 $31.00 © 2014 IEEE DOI
10.1109/EDOCW.2014.61
23. John A. Bullinaria and Khulood AlYahya , “Artificial Bee Colony Training
of Neural Networks” , Nature Inspired Cooperative Strategies for
Optimization (NICSO 2013), Studies in Computational Intelligence 512,
DOI: 10.1007/978-3-319-01692-4_15, © Springer International Publishing
Switzerland 2014
24. Mehdi Kashefikia , Nasser Nematbakhsh, Reza Askari Moghadam ,
“MULTIPLE ANT-BEE COLONY OPTIMIZATION FOR LOAD
BALANCING IN PACKET-SWITCHED NETWORKS”, International
Journal of Computer Networks & Communications (IJCNC) Vol.3, No.5,
Sep 2011.
53

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