Cloud computing – partitioning algorithm

International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.5, October 2014
CLOUD COMPUTING – PARTITIONING ALGORITHM
AND LOAD BALANCING ALGORITHM
Anisaara Nadaph1 and Prof. Vikas Maral2
1Department of Computer Engineering, K.J College of Engineering and Management
Research Pune
2Department of Computer Engineering, K.J College of Engineering and Management
Research Pune
ABSTRACT
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
KEYWORDS
cloud , central controller system (ccs), partition status collector
1. INTRODUCTION
Due to versatile use of internet cloud computing is becoming the back bone of soft computing.
When a server is overloaded the arriving job should be diverted to the server which is in normal
(underloaded) state such that there will be maximum utilization of the available resources. Cloud
computing has given the IT sector new direction for utilization of resources in a organized
manner as a user pay for usage, Cloud computing is a combined technique from the Grid
Computing , utility computing and autonomic computing.
Cloud Architecture can be alienated into 2 section
i. Front end – Client computer or application to connect the back end
ii. Bach End – servers, data center or data storage unit
The two are connected by the network called as Internet, There is a central manger to monitor
the traffic for efficient performance of the system. The architecture for balancing load
depends on whether the system is for static or dynamic as the static system doesn’t store the
current status of the system, it is immaterial for its design in disparity to this the dynamic
system accumulate the current system information and works according to what is the current
status of the system.
DOI : 10.5121/ijcseit.2014.4504 41

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2. LITERATURE SURVEY
This section gives the study of different strategy to partition the cloud also for balancing the load
the different algorithm available
2.1. PartitionigTechnoques
There are many Partitioning techniques such as
i. Relocation Algorithms
ii. Probabilistic Clustering
iii. K-medoids Methods
iv. K-means Methods
v. Density-Based Algorithms
vi. Density-Based Connectivity Clustering
vii. Density Functions Clustering.
All this technique has many drawback due to which it is not efficient for the project.In this paper
we will be simply dividing the server based on geographical location in which we will consider
the Region wise division of server this will simplify to check the traffic rate of server, as some
part of globe have night and other have day due to this the rate of traffic is different for different
region. Cloud computing is growing technology in the field of computer science. Gartner’s [12],
in his report that the cloud will changes the scenario in IT sector. The cloud computing is
changing life with various types of services. Users get service from a cloud without paying
attention to the details [11]. For cloud computing NIST[5] has given a definition as a model for
enabling everywhere, suitable, on-demand network access to a common pool of configurable
computing assets that can be swiftly provisioned and unconfined with minimal management
service provider interaction. Much and more people pay interest to cloud computing[6] .Cloud
computing is resourceful and scalable but maintaining the steadiness of processing numerous jobs
for cloud computing surroundings is a very multifaceted problem with load balancing receiving
much consideration. As we know in dynamic system the job can arrival in a pattern which is not
predictable and the abilities of each node in the cloud vary, for load balancing problem, workload
control is critical to pick up system performance and maintain steadiness. The load balancing
model given in this piece of writing is expected at the public cloud which has abundant nodes for
geographic isolated areas. Thus, this model has number of partition in the cloud for a public
cloud. These partitions make it easy for load balancing when the location is very large and
compound. The cloud has a main manager that prefer the appropriate partitions for incoming jobs
while for every job that is arriving the balancer decides the finest load balancing policy from
Ant colony and Honey Bee load balancing algorithms[3][2].
.
2.2. Load Balancing
Load balancing is a new technique that improves the performance of available networks and
resources by providing a highest throughput with least response time. Dividing the load between
servers, data can be sent and received with negligible delay. A simple example of load balancing
in our daily today life can be concerned with websites. Without load balancing, users could
experience long delays, timeouts and possible delayed system responses time. Load balancing
solutions usually apply redundant servers which help a better distribution of the communication
traffic so that the website is availability without delay. There are different load balancing

algorithms, which can be broadly divided into two groups: static balancing [4] and dynamic load
balancing.
There are many research work of load balancing for the cloud partitioning. Load balancing in
cloud computing was described in a white paper written by Adler[8] who introduced the methods
used for load balancing in the cloud. Load balancing in the cloud is further a new problem that
requires new architectures to adapt to many enhancement. Chaczko et al described the idea that
load balancing plays in improving the performance and maintaining stability
43
2.3. Ant Colony Algorithm
This algorithm is based on the natural behavior of the ant in which a special substance called
pheromone is laid down by the ant who goes out in search of the food and remaining ant follows
the pheromone [13][10], The shortest path is selected in which maximum pheromone is there as
that will be the shortest path [13][10].as the ant select the shortest path the ant colony[4]
algorithm each ant will maintain a record-set and the traffic that is arriving will be diverted to the
path which has the more probability as in natural ant which select a path with highest pheromone.
2.4. Honey Bee Method
A honey-bee colony consists of[9] queen, forager. Forager are of type Employeed( bees with job)
Unemployed(bees without job) where unemployed bee are also called as the scout bee who
moves around in search of food without any knowledge and employed bees are those to whom the
scout bee gives the information and based on the dance performed the patch or group of
unemployed bee moves scout bee moves in all direction in search of food. And they come back
and perform the waggle dance[7] on the dance floor to give the information to the remaining bee
regarding the distance, direction and extent of food that is available based on the dance floor and
accordingly patches(group of honey bee) moves to collect the food.
3. PROPOSED WORK
3.1. System Architecture
There is a central controller system(ccs) towhich all the request arrive and then the Load balance
is selected based on the balancer having minimum load, after that the nodes on which the data to
be processed is selected This selection is based on Ant Colony Algorithm or Honey Bee
Algorithm.
3.2. System Model
There several cloud computing methodologies in this paper we are focusing on the public cloud,
and this cloud (public cloud) is based on the some standard cloud computing architecture, with
service provider providing the service. A large public cloud consists of many nodes and the
nodes in distinct geographical areas. To manage large cloud spread over large geographical areas
cloud partitioning are used. A public cloud that consist of partition with divisions based on the
geographic locations..The load balancing algorithm is based on the cloud partitioning
architecture. After establishing the cloud regions, the load balancing is invoked: when a job
comes at the system, with the central controller system make decision that to which cloud region
the job enters the decision is based on maximum memory available if same amount of memory is
available then on number of request. The partition monitoring controller load balancer then make

decision on strategy to allocate the jobs to the nodes in a particular region. According to the
monitoring controller when the load status of a cloud region is normal, this partitioning can be
managed local way. But if the status of cloud partition load is not normal, job should be
relocating to another region
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3.3. System Flow
This flow diagram explained below provides the systematic description of the entire system with
Ant colony Algorithm and Honey Bee Behavior.Flow as shown in figure , in which first a request
arrives to the central controller system and then depending on the free memory of the load
balancer it is decided as per which balancer is selected and then the node for processing is
selected by Ant colony or Honey bee algorithm, for different size job instances of request the
study for both algorithm is done and comparative result will be shown.
Figure1 Flow Diagram for Load Balancing using Ant Colony Algorithm or Honey Bee Algorithm

45
4. MATHEMATICAL MODEL
This fragment of paper describes the mathematical model for the entire system which includes
selection of Load balancer by the central controller system and then selection of Nodes by the
balancer for processing by Ant Colony Algorithm and Honey Bee Behaviour
4.1 Selection of balancer
The function used for selection of the load balancer[1]
(1)
Where Memory(n) is memory utilized by node, m_utli(B) is the memory utilized by Load
Balancer.
a. If m_utli(B) =0 then the Balancer is in Idle state.
b. If m_utli(B) >0 and <= Threshold Load, Balancer is in Normal state.
c. If m_utli(B) >threshold, Balancer is in Overloaded state.
4.2 Selection of node in Idle state
The selection of node is done in Round Robin fashion as shown in equation(2)
v node(i) : empty(node(i)) select(node (i)) (2)
where i takes the value from a to d for balancer A and a to d for balancer B
where a to d are the nodes attached with the balancer.
4.3. Selection of node in Normal state
4.3.1 Load Balancing by Ant Colony Algorithm[4]
Let the system be defined by S for choice of node using Ant Colony Algorithm on Cloud
Partition. The 5 parameters for the system S selection is given as Input, Output, Function,
Success, Failure, hence it is defined as
S={I,O,F,S,Fi}
where I= input to the system, O= output of the system, F = function used in the system, S=
condition for success, Fi= condition for failure of the system.
For the system I = set of jobs that arrived from the central controller , the output obtained for the
system will be (O)= selection of the node, the system is in success state if the node is obtained
and the system is in failure state if node is not obtained for storage of job.selection of Node using
Ant Colony Algorithm the function F2 [10] is computed as
a. Initially a randomized selection is done is memory space is available then select the node

b. If the randomly selected node does not have sufficient space then the node having
maximum entry in the forward Pheromone table is selected as shown in equation (3) fp is
the table having forward pheromone or value for selection of the next node and max is a
variable used to check for maximum pheromone and it is initialized to 0
46
{ (3)
Max= fp (i)
Next node=i
4.3.2 Load Balancing by Honey Bee Behavior[2]
Let S1 be System for choice of node using Honey Bee Behavior .The system S1 has 5 parameters
in it Input, Output, Function, Success, Failure given as S1= {I, O, F, S, E}.
The input to the Balancer for selection of node by honey bee is job of varying size, Ouptut will
be node selected. The function applied for node selection is in equation(3)
v node(i) : M_Freemax(i) select(node (i)) (4)
where i takes the value from a to d for balancer A and a to d for balancer B
where a to d are the nodes attached with the balancer.
• Preemptive scheduling is needed when the load balancer is selected, but the server nodes
do not have sufficient space for execution of job then the jobs with same priority are
checked and the node which has maximum number of same priority job are selected.
(5)
In equation (5) used Tn is the total number of job of same priority, the Tn value is calculated for
each server for different priority which ranges from 1 to m, and the node having more number of
same priority job as arrived job is assigned for processing.
5. EXPERIMENTAL RESULT
The overall memory of each balancer is calculated my adding the memory space of each server.
Initlally all server are in Idle state and hence a Round Robin algorithm are used whereas when all
server reaches the normal state the Ant Colony optimization or Honey Bee behaviour algorithm
are used. Job is assigned to the central Controller (ccs) then the ccs decides (based on the balancer
with maximum space). the job is transferred to the balancer for this either the Ant Colony or
Honey Bee algorithm is used and for each job with Ant Colony and Honey Bee the time required
to assign the node is calculated for the same the result obtained are as follows. The figure 2 and
figure 3 gives the experimental result when the maximum threshold of the server is set as 100KB
whereas figure 4 and figure 5 gives the result when maximum threshold is set to 300KB.

47
Figure 2 – Ant colony optimization (upper threshold -100)
Figure 3 – Honey Bee behavior (upper threshold -100)
Figure 4 – Ant colony optimization (upper threshold -300)

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Figure 5 – Honey Bee behavior (upper threshold -300)
6. CONCLUSIONS
This paper presented a analysis of Honey Bee and ant Colony load-balancing algorithms for
Cloud partitioning. It was observed that centralized allocation will not efficient for load across all
nodes in a system. So a partitioning approach is required that balances load among network. As
such, Honey Bee and Ant colony algorithms were consider for this comparative analysis of study.
Both (Honey bee foraging and Ant behavior) based on a swarm intelligence. When resource and
system architecture are considered, new issues will arise. Both Ant Colony and Honey Bee appear
better as the number of processing requests is increased. As such, enhanced future work required
for further study, It is found that there are number of algorithms available for load balancing. It is
not known how to select appropriate balancing techniques for given applications that will provide
a suitable configuration for the application. The experiment gives the conclusion that Honey Bee
gives better result than Ant colony optimization techniques
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[9] Brian R. Johnson James C. Nieh, ” Modeling the Adaptive Role of Negative Signaling in Honey Bee
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Intraspecific Competition”, Springerlink.com, 2010
[10] Ratan Mishra1 and AnantJaiswal ”Ant colony Optimization:”A Solution of Load balancing in Cloud”,
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