here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.

1. “Ant Colony Optimization
for Load Balancing of
Nodes in Cloud.”

2. 2
Agenda
 Abstract
 Introduction
 Ant Colony Optimization
 Natural behavior of ant
 ACO for load balancing in cloud
 Literature survey
 Conclusion and Future scope
 References
1

3. Abstract
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.
Cloud load balancing is a type of load balancing that is performed
in cloud computing. Cloud load balancing is the process of
distributing workloads across multiple computing resources.
Ant Colony Optimization is basic foraging behavior of an ant that
encouraged them to find the optimal shortest path from their nest to
food introduced by Dorigo and Gam-bardella
2

4. Introduction
 What is Cloud Computing?
 Internet-based on demand computing.
 Pay-as-you-go
 Accessing Computing resources of Third party
3

5. Characteristics of cloud computing
 Large scale infrastructure
 Virtualization
 High reliability
 Universality
 Easy scalability
 In the form of demand requested service
 Low cost
4

6. Load Balancing in cloud
 Load Balancing is a method to distribute workload across one or more
servers
 Load balancing is used to make sure that none of your existing resources are
idle while others are being utilized.
 To balance load distribution, you can migrate the load from the heavy
loaded nodes to the comparatively lightly loaded destination nodes.
 Goals of Load balancing
 To improve the performance substantially
 To have a backup plan in case the system fails even partially
 To maintain the system stability
5

7. Ant Colony Optimization
 The inspiring source of ACO is the foraging behavior of real ants. When
searching for food, ants initially explore the area surrounding their nest in a
random manner.
 As soon as an ant finds a food source, it evaluates the quantity and the quality
of the food and carries some of it back to the nest.
 During the return trip, the ant deposits a chemical pheromone trail on the
ground. The quantity of pheromone deposited, which may depend on the
quantity and quality of the food, will guide other ants to the food source.
 Indirect communication between the ants via pheromone trails enables them to
find shortest paths between their nest and food sources. This characteristic of
real ant colonies is exploited in ant colony optimization.
6

8. A Practical Scenario
7

9. 8

10. 9

11. 10

12. Con…
 Paths that have the highest pheromone intensity have the
shortest distance between the point and the best food source.
The movements of these ants independently update a solution
set.
 The Traversal of ants in this system is generally of two types:
1) Forward movements-In this type of movement the ants move
for extracting the food, or searching for the food sources.
2) Backward movements-In this type of movements the ants
after picking up food from the food sources traverse back to the
nest for storing their food.
11

13. ACO for load balancing in cloud
 The ACO is used for load balancing.
 Ants Continuously originates from head node and traverse the width and
length of the network.
 These Ants along with their traversal will be updating a pheromone table.
 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 encountering overloaded node or under loaded node.
 2) Backward movement-If an ant encounters an over-loaded 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.
 Main task is to redistribute the work among the nodes.
 Maintain a table for resource utilization. 12

14. Pheromone tables
 Similar to routing table of a network.
 The pheromone strengths are represented.
 Every node has a pheromone table for every possible destination in the
network, and each table has an entry for every neighbour.
 Their most frequent load type is denoted in the bracket (i.e. Overloaded-0,
Medium loaded-M, Under loaded-U) and simultaneously the calculated
pheromone table which indicates the level of particular pheromone type (i.e.
High-H, Low-L, Medium-M) between corresponding nodes.
13

15. Con….
 Here L,M,H shows the probability of moving.
 So the question is that probability in terms of what?
 Probability is based on pheromone concentration. The equation for the
moving probability is shown in next slide. (eq-2)
 And how the pheromone is calculated is based on eq-1.
13

16. What should be the pheromone??
 The value of pheromone here is,
τij(t=0)=f(MIPSJ,L,BWJ)………………eq-1
 Pheromone value in between two node i and j at turn t=0, MIPSJ (Million
Instructions per Second) is the maximum capacity of each processor of VMJ
 The parameter BWJ is related to the communication bandwidth ability of
the VMJ.
 L is the delay cost is an estimate of penalty, which cloud service provider
needs to pay to customer in the event of job finishing actual time being
more than the dead-line advertised.
14

17. Con…..
 The ants traverse the cloud network, selecting nodes for their next step
through the classical formula given below, where the probability Pk of an
ant,which is currently on node r selecting the neighboring nodes for
traversal, is:
 Pk(r, s) =[ τ(r, s)][η(r, s)]^β …………………….eq-2
[τ (r,u)][η(r, u)] ^β
 where, r = Current node,
s = Next node,
τ= Pheromone concentration of the edge,
η = The desirability of the move for the ant (if the move is from an
under loaded node to overloaded node or vice-versa the move
will be highly desirable),
β= Depends upon the relevance of the pheromone con-centration
with the move distance.
15

18. Pheromone Updation
 The ant will use two types of pheromone for its movement. The type of
pheromone being updated by the ant would signify the type of movements
of the ant and would tell about the kind of node the ant is searching for (i.e.
overloaded or underloaded node). The two types of pheromones updated by
the ants are as follows:
1) 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,
βeva = 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
16

19. Con….
2) 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,
βeva = 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
 Since pheromones evaporate and diffuse away, the strength of the trail
when it is encountered by another ant is a function of the original
strength, and the time since the trail was laid. 17

20. Con….
 βeva is pheromone evaporation factor which represents the pheromone volatilization
degree during unit time. Correspondingly, (1- βeva) represents the degree of residual
pheromone. The value of βeva is between [0, 1].
 The greater the value of βeva , pheromone evaporate faster, and past searches has a
small influence on next step searching
18

21. OverloadedUnderloaded
 After originating from head nodes, ant move to node called as nextnode.. The
encountered nextnode status can either be overloaded or under loaded.
 Now this nextnode is became current node.Suppose, the load on
currentnode is greater than threshold i.e. status of currentnode is overloaded.
 Now, ant will search for underloaded node among the neighboring nodes of
the currentnode.
 Here, ant can either get all overloaded neighbors or one underloaded node
with minimum load. If ant get underloaded node then it will move to that
node and update TP otherwise, ant will select the node which has minimum
TP among neighbor nodes of currentnode and then move to that node and
then update TP.
 Now, the node on which ant is moved, is became a currentnode.
 Now, redistribution of load is done if and only if currentnode is
underloaded. Otherwise, it will again search for the underloaded node
among neighbor nodes of currentnode. 19

22. Underloaded  Overloaded
 Now suppose, the load on currentnode is less than threshold i.e. status of
currentnode is underloaded. Now, ant will search for overloaded node among the
neighboringnodes of the currentnode.
 Here, ant can either get all underloaded neighbors or one overloaded node with
maximum load.
 if ant get overloaded node then it will move to that node and update FP otherwise,
ant will select the node which has maximum FP among neighbor nodes of
currentnode and then move to that node and then update FP.
 Now, the node on which ant is moved, is became a currentnode.
 Now, redistribution of load is done if and only if currentnode is overloaded.
Otherwise, it will again search for the overloaded node among neighbor nodes of
currentnode.
20

23. Flowchart
FP:Foraging Pheromones
TP:Trailing Pheromones
Flowchart

24. Literature survey
Paper Description Pros Cons
Load Balancing of
Nodes in Cloud Using
Ant Colony
Optimization
IEEE 2012 14th
International
Conference on
Modeling and
Simulation
Ants
continuously update a
single result set rather
than updating
their own result set
•System can continue
functioning properly
even at peak usage
hours
• It gives optimum
solution of load.
• it is centralized
no single point of
failure
•.Network overhead
because of the large
number of ants
•Points of initiation
of ants and number
of ants are not clea
A Technique Based on
Ant Colony
Optimization for Load
Balancing in Cloud
Data Center
IEEE 2014 13th
International
Conference on
Information Technolog
It uses redistribution
policy and considers
number of requests.
•high availability of
resources
•increasing the
throughput
•maximum resource
utilization.
•It doesn’t consider
server’s CPU power,
memory etc
22

25. Con….
Paper Description Pros Cons
An Ant Colony Based
Load Balancing
Strategy in Cloud
Computing
 Springer 2014
Advanced Computing,
Networking and
Informatics - Volume 2
soft computing based
algorithm on ant colony
optimization has been
proposed to initiate the
load balancing under
cloud computing
architecture
•guarantees the QoS
requirement
• Fault tolerance issues
does not consider
• All jobs are predicted
with same priority here,
which may not be the
actual scenario.
Ant colony
Optimization: A
Solution of Load
balancing in Cloud
International Journal
of Web & Semantic
Technology (IJWesT)
2012
A heuristic
algorithm based on
ant colony
optimization has been
proposed
• The pheromone
update mechanism has
been proved as a
efficient and effective
tool to balance the load.
This modification
supports to minimize
the make span of the
cloud computing based
services.
• This technique does
not consider the fault
tolerance issues.
23

26. Conclusion
 This load balancing technique based on Ant Colony Optimization gives
optimal resource utilization.
 The performance of the system is enhanced with high availability of
resources, thereby increasing the throughput.
 This increase in throughput is due to the optimal utilization of resources
 Future work is to implement this technique with the consideration of
server’s CPU power, memory etc while redistributing load and also to do
implementation of algorithm for finding out neighbors of a node with
particular Data Center Network Architecture.
Future work
24

27. References:
 Nitin and Ravi Rastogi, Kumar Nishant, Pratik Sharma, Vishal Krishna,Chhavi
Gupta and Kunwar Pratap Singh,Load Balancing of Nodes in Cloud Using Ant
Colony Optimization, IEEE 2012 14th International Conference on Modelling and
Simulation
 Ekta Gupta , Vidya Deshpande . A Technique Based on Ant Colony Optimization for
Load Balancing in Cloud Data Center ,IEEE 14 13th International Conference on
Information Technology.
 Shagufta khan and Niresh Sharma, Ant Colony Optimization for Effective Load
Balancing In Cloud Computing, ISSN 2278-6856 IJETTCS
 Santanu Dam, Gopa Mandal,Kousik Dasgupta,and Paramartha Dutta.An Ant Colony
Based Load Balancing Strategy in Cloud Computing. Springer International
Publishing Switzerland 2014
 Klaithem Al Nuaimi, Nader Mohamed, Mariam Al Nuaimi and Jameela Al-Jaroodi
.A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms.
2012 IEEE Second Symposium on Network Cloud Computing and Applications
 Ratan Mishra and Anant Jaiswal Ant colony Optimization: A Solution of Load
balancing in Cloud. International Journal of Web & Semantic Technology (IJWesT)
Vol.3, No.2, April 2012.
 https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms 25