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CHAPTER 5

PARALLEL GENETIC ALGORITHM AND COUPLED

APPLICATION USING COST OPTIMIZATION

5.1 INTRODUCTION

Cloud Computing provides on demand access of resources over the

network. The main characteristics of virtualization technologies employed in

Cloud atmosphere is the consolidation and proficient management of the

resources. The current work employs an optimized scheduling algorithm,

which concentrates on the efficient utilization of the resources for the cloud

scheduling problems. A Parallel Genetic Algorithm with the Dynamic Deme

model is used for scheduling the resources dynamically. The investigation

shows that the scheduling procedure improves the utilization rate of the

system resources and also the pace of allotment of the resource. The user

could access the computing resources as general utilities, which can be

acquired and released at any time.

Access to Cloud resources easily enables the simultaneous use of

many clouds. The system analyzes the viability from the view point of

scalability, performance, and cost of deploying large virtual cluster

infrastructures distributed over different cloud providers for solving loosely

coupled Many Task Computing (MTC). The performance of different cluster

configurations can be evaluated using the cluster throughput as a performance

metric.

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5.2 PARALLEL GENETIC ALGORITHM FOR RESOURCE

SCHEDULING IN CLOUD

The resource scheduling is a crucial process in cloud applications

such as IaaS. The existing approach worked with Parallel Genetic Algorithm

(PGA) used for resource allocation and utilization of system resources.

Thereby in the proposed model addressed a novel approach called PGA with

Dynamic Deme, efficiently scheduling the resources in cloud environment

dynamically.

The most important advantage of PGAs is that in many cases they

provide better performance than single population-based algorithms, even

when the parallelism is simulated on conventional machines. The reason is

that, multiple populations permit speciation, a process by which different

populations evolve in different directions. For these reasons PGAs are not

only an extension of the traditional GA sequential model, but they represent a

new class of algorithms in which, they search the space of solutions

differently.

This proposed work focuses on the analysis of the performance of

the Dynamic Demes algorithm for cloud resource scheduling in an efficient

manner. The investigation shows that, the scheduling procedure improves the

utilization rate of the system resources and also the pace of allotment of the

resource.

5.2.1 Architecture Diagram

The above architecture explains the architecture new of Parallel

Genetic Algorithms (PGA) which is scalable to the large systems, commonly

found in clouds. The initial step begins with analyzing the process using the

simulation kit. From that, the input resources and instead request can be

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divided which are the resource of PGA with Dynamic Deme Model. The next

block consist of PGA with coarse grained and Dynamic Deme model which

well do the optimization of resource allocation and the allocated resources are

located in the allocation sequence block. These block resources performance

parameters may be gathered and sent to the performance analysis block. The

actual performance evaluation can be carried out in the performance analysis

model. The next block is called as performance report block which is meant

for logging performance. After taking the performance report, the allocation

of resources will be terminated which is referred in the termination block

Figure 5.1 Architecture Diagram of Parallel Genetic Algorithm

Initiate the Process using the Simulation tool

Input Resource & Instance Request

PGA Scheduler with Coarse-Grained & Dynamic Deme model

Allocation Sequence Performance Analysis

Performance Report

Termination

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5.2.2 Methodology

The management of a cloud leaves providers with difficult tasks of

dynamically provisioning a large-scale system to meet customer’s demands.

Traditional optimization techniques cannot properly handle the scale of

leading cloud environments. The research examines stochastic optimization

strategies using Parallel Genetic Algorithm which are scalable to the large

systems and commonly found in clouds to optimize utilization of available

servers and improve the timely service of customer requests.

The basic idea behind most parallel programs is to divide a task

into chunks and to solve the chunks simultaneously using multiple processors.

This divide-and-conquer approach can be applied to GAs in many different

ways, and the literature contains many examples of successful parallel

implementations. Some parallelization methods use a single population, while

others divide the population into several relatively isolated subpopulations.

Some methods massively exploit parallel computer architectures, while others

are better suited to multicomputer with fewer and more powerful processing

elements. A novel attempt of implementing Parallel Genetic Algorithm with

the Dynamic Deme model has been absorbed in the current work for

scheduling the resources.

This method principally aims at allocating the resources, in a more

competent way by utilizing the available resources in Cloud Environment

(IaaS). Allocation of the resource is based on the instance request, provided

by the user. The PGA Scheduler uses the Dynamic Deme Model of the

Parallel Genetic Algorithm for scheduling the resources. The project is

implemented in java language with the help of the Integrated Development

Environment (IDE) Jcreator.

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5.2.2.1 Genetic Algorithm

Genetic algorithms are inspired by Darwin's theory about evolution.

Genetic Algorithms (GAs) are efficient search methods based on principles of

natural selection and genetics. GAs are generally able to find good solutions

in reasonable amount of time, but as they are applied to harder and bigger

problems, there is an increase in the time required to find adequate solutions.

As a consequence, there have been multiple efforts to make GAs faster, and

one of the most promising choices is to use parallel implementations.

Components of a Genetic Algorithm

• Encoding technique

• Initialization procedure

• Evaluation function

• Selection of parents

• Genetic operators

A typical algorithm might consist of the following:

A number of randomly chosen guesses of the solution to

problem - the Initial Population.

A means of calculating how good or bad each guess is within

the population - a Population Fitness Function.

A method for mixing fragments of the better solutions to form

new and on average even better solutions - Crossover.

An operator to avoid permanent loss of (and to introduce new)

diversity within the solutions - Mutation.

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5.2.2.2 Parallel Genetic Algorithm

For some kind of problems, the population needs to be very large

and the memory required to store each individual may be considerable. In

some cases this makes it impossible to run an application efficiently using a

single machine, so some parallel form of GA is necessary. Fitness evaluation

is usually time-consuming and the only practical way to provide the required

CPU power is to use parallel processing. The most important advantage of

Parallel Genetic Algorithms (PGA) is that in many cases they provide better

performance than single population-based algorithms, even when the

parallelism is simulated on conventional machines. The reason is that,

multiple populations permit speciation, a process by which different

populations evolve in different directions. For these reason Parallel GAs are

not only an extension of the traditional GA sequential model, but they

represent a new class of algorithms in which they search the space of

solutions differently.

Master-Slave Parallelisation: Master Slave Parallelisation

method, also known as distributed fitness evaluation, is one of

the first successful applications of parallel GAs. It is also

known as global parallelisation, master-slave model or

distributed fitness evaluation.

The algorithm uses a single population and the evaluation of

individuals and the application of genetic operators are

performed in parallel. The selection and mating is done

globally, hence each individual may compete and mate with

any other individual. The operation that is most commonly

parallelised is the evaluation of the fitness function, because,

normally it requires only the knowledge of the individual

being evaluated (not the whole population), and so there is no

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need to communicate during this phase. This is usually

implemented using master slave programs, where the master

stores the population and the slaves evaluate the fitness, apply

mutation, and sometimes exchange bits of the genome (as part

of crossover). Parallelisation of fitness evaluation is done by

assigning a fraction of the population to each of the processors

available (in the ideal case one individual per processing

element). Communication occurs only as each slave receives

the individual (or subset of individuals) to evaluate and when

the slaves return the fitness values, sometimes after mutation

has been applied, with the given probability.

The algorithm is said to be synchronous, if the master stops

and waits to receive the fitness values for all the population,

before proceeding with the next generation. A synchronous

master-slave GA has exactly the same properties as a simple

GA, except for its speed, i.e. this form of parallel GA carries

out exactly the same search as a simple GA.

An asynchronous version of the master-slave GA is also

possible. In this case, the algorithm does not stop to wait for

any slow processors. For this reason the asynchronous master

slave PGA does not work exactly like a simple GA, but is

more similar to parallel steady-state GAs. The difference lies

only in the selection operator. In an asynchronous master-

slave algorithm, selection waits until a fraction of the

population has been processed, while in a steady-state GA

selection does not wait, but operates on the existing

population. A synchronous master-slave PGA is relatively

easy to implement and a significant speedup can be expected,

if the communication cost does not dominate the computation

cost.

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Drawback: However, there is a classical bottle-neck effect.

The whole process has to wait for the slowest processor to

finish its fitness evaluations. After that, the selection operator

can be applied. The asynchronous master-slave PGA

overcomes this, but as stated before, the algorithm changes

significantly the GA dynamics, and as a result it is difficult to

analyse.

Subpopulations with Migration: The important

characteristics of the class of static subpopulations with

migration parallel GAs are the use of multiple demes and the

presence of a migration operator. Multiple-deme GAs is the

most popular parallelization method, and many concepts have

been proposed describing details of their implementation.

These algorithms are usually referred to as subpopulations

with migration, static subpopulations, multiple-deme GAs,

coarse-grained GAs and even just parallel GAs. This

parallelisation method requires the division of a population

into some number of demes (subpopulations). Demes are

separated from one another (geographic isolation), and

individuals compete only within a deme. An additional

operator called migration is introduced from time to time;

some individuals are moved (copied) from one deme to

another. If individuals can migrate to any other deme, the

model is called an island model. If individuals can migrate

only to neighbouring demes, it is termed as stepping stone

model. There are other possible migration models.

The migration of individuals from one deme to another is the

topology that defines the connections between the

subpopulations. Commonly used topologies include:

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hypercube, two-dimension, three-dimensional mesh, torus,

etc. The migration rate controls how many individuals migrate

in a migration scheme, and also controls which individuals

from the source deme (best, worst, random) migrate to another

deme, and which individuals are replaced (worst, random,

etc.). A migration interval determines the frequency of

migrations.

Coarse grained algorithms are a general term for a

subpopulation model with a relatively small number of demes

with many individuals. These models are characterised by

relatively long time as they require for processing a generation

within each (“sequential”) deme, and by their occasional

communication for exchanging individuals. Sometimes coarse

grained parallel GAs is known as distributed GAs, because

they are usually implemented on distributed memory Multiple

Instruction Multiple Data (MIMD )computers. This approach

is also well suited for heterogeneous networks.

Fine grained algorithms function is the opposite way. They

require a large number of processors, because the population

is divided into a large number of small demes. Inter-deme

communication is realised, either by using a migration

operator, or by using overlapping demes. Recently, the term

fine-grained GAs, was redefined and is now used to indicate

massively parallel GAs.

Constraint: The multiple-deme model presents one problem:

scalability. If one has only a few machines, it is efficient to

use a coarse grained model. However, if one has hundreds of

machines available at a time, it is difficult to scale up

efficiently the size and number of subpopulations, to use the

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hardware platform efficiently. Despite this problem, the

multiple-deme model is very popular. From the

implementation point of view, multiple-deme GAs are simple

extensions of the serial GA. It’s enough to take a few

conventional (serial) GAs, run each of them on a node of a

parallel computer, and to apply migration, at some

predetermined times.

Dynamic Demes: Dynamic Demes is a new parallelization

method for GAs which allows the combination of global

parallelism with a coarse-grained GA. In this model, there is

no migration operator as such, because the whole population

is treated during evolution, as a single collection of

individuals, and information between individuals is exchanged

via a dynamic reorganization of the demes, during the

processing cycles. From the parallel processing point of view,

the dynamic demes approach fits perfectly the MIMD

category (Flyn classification) as an asynchronous multiple

master-slave algorithms. The main idea behind this approach

is to cut down the waiting time for the last (slowest)

individuals to arrive in the master-slave model, by

dynamically splitting the population into demes, which can

then be processed without delay. This is efficient in terms of

processing speed.

In addition, the algorithm is fully scalable. Starting from a

global parallelism with fitness-processing distribution, one can

scale up the algorithm to a fine grained version, with few

individuals within each deme and big numbers of demes. The

algorithm can be used on shared and distributed memory

parallel machines. Its scalability can prove vital in systems

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with a few Processing Elements, as well as, in massively

parallel systems with large number of Processing Elements,

and everything in between. Dynamic Demes (DD) is scalable

and an easily implemented method of GA parallelization.

Advantage: The main advantages of dynamic demes are:

High scalability and flexibility (DDs can be used to

implement a broad range of algorithms from coarse

grained to highly fine grained models)

Fault tolerance (some of the processors can crash, but the

algorithm will correctly continue the operation)

Dynamic load balancing and Easy monitoring.

Algorithm Description: Each individual is represented by a

separate process called as slave, which is capable of

performing the following:

i) Fitness evaluation

ii) Applying mutation to itself (with a predefined mutation

rate)

iii) Performing crossover with another individual (this is

done by passing to each individual, the process ID of

another individual, with which it should perform

crossover)

All the individuals run concurrently. The ideal case is when a single

processing element processes a single individual. There are additional

processes, called masters, which are responsible for selection and mating.

Masters handle a fixed fraction of the population and apply selection and

mating on it. Therefore, each master represents a separate deme. However,

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unlike other PGAs, in Dynamic Deme (DD) the individuals belonging to each

deme, change dynamically. The number of masters is a parameter of the

algorithm. If there is only one master, DDs function as a classic distributed

fitness evaluation algorithm. Each master process performs selection and

mating concurrently with the other master.

Mating requires sending the appropriate slave ID to the individuals

chosen for crossing over. When the slaves receive a partner ID they perform

crossover, and then proceed with fitness evaluation and mutation. In addition

to masters and slaves, there is also a process (possibly more) responsible for

load balancing, called counters. After crossover, fitness evaluation and

mutation, each individual is dynamically assigned to a deme (possibly

different from the one it belonged to previously). This happens when the

individual notifies the counter process. The counter process knows which

master processes are currently, idle waiting for their subpopulation to be filled

and it sends to the individual the process ID of one such master.

The last process within the system is called sorter. This process is

informed by all of the individuals finishing their evaluation, takes their

genotype and fitness, and saves them in appropriate log files. The sorter

process is also responsible for stopping the search, when a termination

criterion is met.

5.2.2.3 Modules

The flow of the work consists of three different modules, which

comprises of resource specification followed by the execution of the PGA,

resulting in the allocation sequence. The virtual machine request is provided

as the input parameter to the system, along with the number of iterations to be

carried out for the execution of the genetic algorithm. Based on the virtual

machine, specified cloudlets are created. The scheduling of the resource is

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carried out using the parallel genetic algorithm and the final allocation

sequence of the resources to the instance request, is obtained when the

simulation ends.

5.2.2.4 Modules Description

i) Creation of Cloud Environment: The initial process is used

to create the Cloud Environment for the execution of the

algorithm with the help of the simulation tool.

ii) Resource Listing: The available resource list, is updated

when allocation or de-allocation of the resources takes place.

The request from the clients are collected and updated in the

VM request list, when ever new VM requests makes it arrival.

Each request is identified by a separate VM id. The instance

request is provided in terms of Cloudlet id’s. The Cloudlet id

is defined in the system based on the Virtual Machine request.

iii) Sequencing: A Parallel Genetic Algorithm is implemented

using Dynamic Deme Model, to calculate the fitness and to

find the optimal allocation sequence, among the available pool

of resource. The Genetic operation is performed using the

concept of threads. A thread, called Slave, performs the,

Fitness evaluation, Mutation and Crossover operation.

Another thread, called Master, performs the Selection

operation and mating process. The third thread is used to

perform the load balancing operation and the last thread

indicates the process of termination. Based on the instance

request, the PGA finds the optimal resource from the available

resources.

iv) Allocation: This module focus on launching the optimal

resource provided by the PGA to the corresponding Instance

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requests. The VM id which is more optimal to the instance

request is being assigned. Likewise all the VM id’s are

assigned to the corresponding instance requests based on the

expected resource.

Figure 5.2 Modules of the Simulator

5.2.2.5 Procedure for PGA Implementation

The implementation of the proposed concept is in the form of a

simulation using the simulation tool CloudSim.In the simulation, a PGA;

simulator is simulated, which acts as a scheduler for the cloud. The goal of the

“Scheduler” is to find out the allocation sequence to each computing node, in

a cloud, so that, the instances run on proper physical computers. The

automated scheduling model is being divided into three steps. First, the

scheduler updates the available resource list, when allocation or de-allocation

happens and update the VM request list when each time new VM requests

come. Then, the scheduler uses a PGA to find out a fitness and economical

allocation. Finally, the cloud launches the corresponding VMs at the physical

resource for the VM request.

IRs Request

SchedulerPGA Algorithm

Allocation Listing

Computing Resource

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To run a GA, the two most important factors are Chromosome

Representation and Fitness Function Evaluation.

Chromosome representation: The integer notation is used to

represent the computing resources. The chromosome pattern

represents the corresponding IRs with the VMs. (7 2 1). The

first request is assigned to VM id 7 and the second to the VM

id 2 and so on.

Fitness Function Evaluation: Fitness function provides the

mechanism for evaluating each chromosome in the problem

domains. It is calculated as the summation process and is used

to select the best resource series. Upon selecting the best

resource series the basic genetic operations are performed.m n

ij ijj 1 i 1

F C X where i= 1, 2,…n, j=1,2,..m; (5.1)

F is the total fitness of an allocation scheme, m represents the

node and n represents the instance request. The value of Xij is

either 0 (if Ith IR is not assigned to Jth node) or 1 (if Ith IR is

assigned to Jth node)

3

ij KK 1

C P (5.2)

where PK = a if VMK / node K = 1

b if VMK / node K < 1

c if VMK / node K > 1

K is a label, when k is equal to 1, it represents the CPU, and

when k is equal to 2, it represents the Memory capacity,3

represents the capacity of the disk. C is the fitness of assigning

ith IR to jth node, and F is the total fitness of an allocation

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scheme. Finally, the calculated fitness value is added with a

big number to get a positive value.

Genetic Operations: The basic operations include replication,

crossover and mutation. Usually single point crossover is

performed. The above mentioned procedure is repeated

concurrently till the optimal solution is obtained.

Steps for Dynamic Deme Algorithm Execution:

1. Input: Initial population of individuals

2. Evaluate the fitness of all individuals

3. While termination condition not met

do

{

4. Slave process performs

i. Fitness Evaluation

ii. Mutation

iii. Crossover

5. Master process performs

i. Selection

ii. Mating

6. Counter process, performs the dynamic re-organization

of population.

7. Sorter process, copies the fitness values and also

responsible for terminating the searching process.

}

End while

where master and slave process are performed concurrently.

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5.2.2.6 Performance Evaluation

The performance evaluation of PGA with DD model was compared

with the existing PGA algorithm to evaluate the performance metric such as

evaluation time for resource allocation based on the various resource requests

for allocating the resources. Figure 5.3 shown below has given the detail of

the Cloud Environment created for proposed Dynamic Deme model by using

the CloudSim. Also Figure 5.4 denotes the Simulation of DD algorithm for

the resource allocation using PGA.

Figure 5.3 Cloud Environments for Dynamic Deme

Figure 5.4 Simulations for Dynamic Deme Algorithm

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Table 5.1 shown below explains the evaluation time required for

the number of tests conducted for resource allocation using existing PGA

method and proposed PGA with DD model. The results expressed the

evaluation time consumption for resource allocation using PGA with DD is

comparatively better than the existing approach.

Table 5.1 Performance Evaluation for the Resource Allocation in PGA

with DD

PGA PGA with DD

No of Test Evaluation Time (ms) No of Test Evaluation Time

(ms) 5 3000 5 2800

6 3800 6 3200

7 4000 7 3700

8 4500 8 4200

9 5000 9 4600

10 5600 10 4800

11 6050 11 5100

12 6400 12 5300

Based on the obtained values of evaluation time for the number of

tests conducted, the graph as been plotted as shown in Figure 5.6, with

representing number of tests in X-axis and evaluation time in Y-axis

respectively. The performance curve clearly explains that the proposed

method consumed very less time to allocate the resources in cloud

environment.

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Figure 5.5 Evaluation Time for Number of Tests

5.3 COUPLED APPLICATIONS USING COST OPTIMIZATION

Cloud computing technologies can offer important benefits for IT organizations and data centers running MTC applications. The challenges and viability of deploying computing clusters are analyzed in the earlier system

for loosely coupled MTC applications with the help of three different Cloud networks like private, public and hybrid. The system analyzes the performance of different cluster configurations, using the cluster throughput as performance metric.

The Multi Cloud deployment involves several challenges. A

performance and cost analysis for different configurations of the real implementation, of a multi-cloud cluster infrastructure, running a real

workload. However, due to hardware limitations in local infrastructure, and the high cost of renting many cloud resources for long periods, the tested

cluster configurations, are limited to a reduced number of computing resources (up to 16 worker nodes in the cluster), running a reduced number of

tasks (up to 128 tasks).

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The upfront challenge are the constraints of Cloud Interface

Standard, the distribution and management of the service master, images and

interconnects the links between the service components. The clusters are

deployed in a hybrid setup, which combines local physical nodes with virtual

nodes, deployed in another compute cloud. Comparing the different cluster

configurations, and proving the viability of the MultiCloud solution proves

cost effective.

5.3.1 Architecture Diagram

Figure 5.6 Hybrid Cluster Architecture Diagram

A new approach for hybrid cluster called Path Clustering Heuristic

(PCH) algorithm is used for the initial Schedule scheme, to overcome the

above stated problem and to achieve cost optimization. The Hybrid Cloud

systems are a novel research challenge, which comes together with the

merging of private and Public Clouds. In this method, the different cluster

configurations are considered with PCH algorithm dynamically and the

cluster nodes can be provisioned with resources from different clouds, to

improve the cost effectiveness of the deployment, or to implement high-

availability strategies.

Submitted Jobs for Processing

Clustering Jobs

Private Cluster Public Cluster Hybrid Cluster

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5.3.2 Methodology

Implementation of PCH algorithm has different modules which are

as follows:

5.3.2.1 Modules

The modules are,

i) Creation of Cloud Environment

ii) Implementation of Scheduling process in Private Cloud

iii) Implementation of Scheduling process in Public Cloud

iv) Implementation of Scheduling process in Hybrid Cloud using

PCH algorithm

5.3.2.2 Modules Description

The module descriptions for the above stated modules are as

follows:

i) Creation of Cloud Environment: Creating a Cloud network

model, for the simulation of new approach is the initial step.

Cloud computing paradigm is being widely used for the

execution of many types of applications, including ones with

data dependencies, which can be represented by workflows.

To execute such workflow applications, in a hybrid cloud, the

scheduling algorithm must take cost into consideration and

execution time. Cost and execution time play an important

role in the cloud environment.

High-availability and fault tolerance: The cluster

worker nodes can be spread on different cloud sites. In

case of cloud downtime or failure, the cluster operation

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will not be disrupted. Furthermore, in this situation, it is

admissible to dynamically deploying new cluster nodes

in a different cloud, to avoid the degradation of the

cluster performance.

Infrastructure cost reduction: Different cloud

providers can follow different pricing strategies, and

even variable pricing models (based on the level of

demand of a particular resource type, daytime versus

night-time, weekdays versus weekends, spot prices, and

so forth), the different cluster nodes can change

dynamically their locations, from one cloud provider to

another one, in order to reduce the overall infrastructure

cost.

A flexible and generic cluster architecture that combines

the use of virtual machines and cloud computing,

dynamically delivers in the heterogeneous computational

environments. Moreover, the introduction of a new

virtualization layer between the computational

environments and the physical infrastructure, makes it

possible, to adjust the capacity, allocated to each

environment and to supplement them with resources,

from an external cloud provider.

ii) Implementation of Scheduling Process in Private Cloud:

The Scheduling process for Private Cloud network model is

implemented. Private Cloud is infrastructure operated, solely

for a single organization, whether managed internally or by a

third-party and hosted internally or externally. Here, resources

that can be accessed and used by individuals inside an

organization, that is similar to data farms or private grids. Also

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it tries to balance the use of private resources, with the ones

available from the Public Cloud.

Private Cloud (also called internal cloud or corporate cloud) is

a marketing term, for a proprietary computing architecture,

that provides hosted services to a limited number of people

behind a firewall. Real-time monitoring of the condor job

queue and virtual machines that belong to individual Virtual

Organizations are provisioned and booted. Jobs belonging to

each Virtual Organization are then operator on the

organization specific virtual machines, which form a cluster,

dedicated to the specific organization.

Once the queued jobs have been executed, the virtual

machines are terminated, thereby allowing the physical

resources to be re-claimed. Tests of this system were

conducted using synthetic workloads which demonstrate that,

dynamic provisioning of virtual machines preserves system

throughput for all. The shortest-running of grid jobs, without

undue increase in scheduling latency and the deployment

requires root privileges on remote resources, which have made

difficult when dynamic deployment is done on those sites.

iii) Implementation of Scheduling Process in Public Cloud:

The implementation of the Scheduling process for the Public

Cloud network model is considered in this module. A Public

Cloud is the one, based on the standard cloud computing

model, in which a service provider makes resources, such as

applications and storage, and is available to the general public

over the internet.

Public Cloud services may be free or offered on a pay-per-

usage model. Public Cloud describes cloud computing in the

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traditional mainstream sense, whereby resources are

dynamically provisioned to the general public on a fine-

grained, self-service basis over the internet, via web

applications/web services or from an off-site third-party

provider who bills on a fine-grained utility computing basis.

To users and applications, the process of borrowing nodes is

transparent.

A VM running as part of a VioCluster is practically

indistinguishable from a physical machine running inside the

same domain. Dynamic machine trading is activated between

mutually isolated virtual domains. VioCluster creates

software-based network components, which seamlessly

connect physical and virtual machines, to create isolated

virtual domains. Machines can be traded dynamically, through

the on-demand creation, deletion, and configuration of VMs

and network components.

Dynamic negotiation of machine trades: Each virtual

domain includes a machine broker which interacts with

other domains. Requests and offers are made through

these brokers based on workload and configurable

lending and borrowing policies. Building a prototype of

the VioCluster system, have demonstrated its

effectiveness using two independent Portable Batch

System (PBS) based job-execution clusters. The

performance evaluation results show benefits to both

clusters by increasing their resource utilization and

decreasing their job execution times.

Physical Domain: An autonomous set of networked

computers is managed as a unit. Physical domains have a

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single administrator, and support a user-base, performing

specific computational activities. For example, a physical

domain belonging to a biology department may be

optimally configured for cellular simulations, while a

physical domain belonging to a network research group,

may be designed for shorter network intensive

experiments.

Virtual Domain: An autonomous set composed of

virtual and physical machines, is managed as a unit.

Machines in a virtual domain are connected through a

virtual private network, to which both virtual and

physical machines have access. Virtual domains are able

to grow and shrink on demand, and to the administrator

they appear to be identical as that of physical domains. A

one-to-one mapping exists between physical and virtual

domains; every virtual domain is hosted upon a physical

domain.

Machine Broker: It is a software agent that represents a

virtual domain when negotiating trade agreements with

other virtual domains. A machine broker consists of a

borrowing policy which determines, under which

circumstances, it will attempt to obtain more machines,

and a lending policy, which governs, when it is willing to

let another virtual domain, make use of machines within

its physical domain. Both policies are defined by the

domain’s administrator.

iv) Implementation of Scheduling Process in Hybrid Cloud

using PCH Algorithm: A new approach for hybrid clusters

called Path Clustering Heuristic (PCH) algorithm is

introduced for the initial schedule scheme. The Hybrid Cloud

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systems are a novel research challenge that comes together

with the merging of Private and Public Clouds. It checks the

private resources whether it already satisfies the deadline.

Deploying a Hybrid Cloud, offers support or automatic service

installation in the resources, which are dynamically provided

by the grid or by the cloud, to execute the PCH algorithm. In

PCH algorithm, all the information necessary to compute

these attributes, are given by the programming model or by

the infrastructure.

New cluster management architecture for shared mixed-use

clusters is followed. The key feature of Cluster-on-Demand

(COD) is supporting configurable dynamic virtual clusters,

which associates variable shares of cluster resources with

application service environments, e.g., batch schedulers and

other grid services. The COD site manager assigns nodes to

‘v’ clusters according to demand and site policies, based on

dynamic negotiation with a pluggable service manager for

each dynamic ‘v’ cluster. Experimental results with the COD

prototype and a service manager for the Sun Grid Engine

(SGE) batch service demonstrates the potential of dynamic

virtual clusters and resource negotiation as a basis for dynamic

provisioning and other advanced resource management

operations, for future grid systems. The results prove that the

key needs for grid resource management can be met directly

by generic site management features which are independent of

any specific application or middleware environment.

A Well Known Address (WKA) based on membership

discovery and management scheme can be used on

environments where multicasting is not possible. There are

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one or more members which are assigned with well known IP

addresses. All other members are aware about these well

known members. At least one well known member should be

started up before any other member. It is also possible to

assign a well known address to the member which started up

first. An elastic IP address can be assigned to this first

member. When other members boot up and try to contact one

or more well known members the WKA has the ability to send

a JOIN message.

The well known, member will add this new member to its

membership list, and notify all other members about this new

member who has joined, by sending a MEMBERJOINED

message to the group, and will send the MEMBERLIST

message to the newly joined member. Now, all group

members will be aware about this new member who has

joined, and the new member will learn about its group

membership. Auto scaling, Axis2, Web service applications

on Amazon EC2, are a very appealing ideas from a business

point of view. Such an approach, makes efficient usage of

resources on a cloud computing environment, and achieves an

optimal balance between performance, cost and availability &

scalability guarantees.

An assumption of a virtual homogeneous system is composed

of an unbounded number of best available processor

connected by links with the highest available bandwidth. Each

task is scheduled on a different processor on the virtual

system, and then the algorithm computes the initial attribute

values of each node. The decision is based on performance,

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cost, and the number of services to be scheduled, in the

Hybrid Cloud using PCH algorithm.

5.3.2.3 Performance Evaluation

A new approach is evaluated with the earlier approaches for

identifying the utilization of resources. The system will analyze and compare

the performance offered by different configurations of the computing cluster,

and the evaluation comparison was performed by evaluating the parameter

metrics such as the viability, from the point of view of, Scalability, Execution

time, Performance and Cost. Based on the comparison and results, it is clear

that the proposed new approach works better than the other earlier systems.

Figure 5.7 shown below represents the creation of Hybrid Cloud

Environment which consists of Cloudlet and VM creation for performance

evaluation.

Figure 5.7 Cloudlet and Virtual Machine Creation

Figure 5.8 has given the details of simulation results of the Cost

optimizing techniques in Hybrid PCH.

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Figure 5.8 Simulation Result of the Cost Optimizing Technique

The performance evaluations of various metrics stated above are as

follows by comparing the both the Hybrid with PCH and Hybrid Cloud.

Table 5.2 shown below gives the cost optimization for the number of

tasks performed to the utilization of resources in Hybrid with PCH and

Hybrid Cloud. The cost optimization for the proposed work is less when

compared to the existing system of the cloud environment.

Table 5.2 Cost Optimization for the Number of Task

Hybrid with PCH Hybrid Cloud No. of Task Cost No. of Task Cost

10 7200 10 450014 8700 14 500015 10,000 15 600025 15,000 25 12,00050 25,500 50 16,50080 32,500 80 25,000

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Figure 5.9 illustrates the graphical representation of cost

optimization for the number of tasks executed to the utilization of resources in

both Hybrid with PCH and Hybrid Cloud. The X-axis denotes Number of

Task and Y-axis denotes Cost respectively. Cost optimization is

comparatively less which is shown in the graphical representation.

Figure 5.9 Cost optimization for different Tasks

Table 5.3 shown below given the throughput for the utilization of

resources carried out for the number of tasks for both Hybrid with PCH and

Hybrid Cloud. Here, the throughput of the proposed system is comparatively

higher than that of the existing system.

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Table 5.3 Throughput obtained for various task

Hybrid with PCH Hybrid Cloud No of Task Throughput No of Task Throughput

10 500 10 60014 650 14 75015 1200 15 135025 1500 25 160050 2200 50 250080 3000 80 3050

100 5000 100 95000

Figure 5.10 represents the throughput for the number of tasks in

both Hybrid with PCH and Hybrid Cloud in the cloud environment. Increase

in throughput leads to the performance improvement of the system. The

graphical representation of the throughput is depicted with Number of Task in

the X-axis and Throughput in the Y-axis.

Figure 5.10 Throughput obtained for different Tasks

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The scalability values of the systems for the utilization of resources

are shown in Table 5.4 for the number of tasks. Here, the efficiency of the

system is increased with that of the scalability.

Table 5.4 Scalability obtained for various task

Hybrid with PCH Hybrid Cloud No. of Task Scalability No. of Task Scalability

10 600 10 65014 750 14 77015 900 15 92025 1100 25 120050 1250 50 125080 2200 80 2250

100 3000 100 5000

Scalability for the number of tasks obtained for both Hybrid with

PCH and Hybrid Cloud in the cloud environment is demonstrated in

Figure 5.11 with X-axis representing the number of task and Y-axis

representing the scalability respectively.

Figure 5.11 Scalability of the system for different Tasks

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The resource utilization value of the both, Hybrid with PCH and

Hybrid Cloud is shown in Table 5.5 representing the values of utility usage

for number of tasks.

Table 5.5 Resource Utilization for the Number of Task

Hybrid with PCH Hybrid Cloud

No. of Task Utility No. of Task Utility

10 700 10 800

14 750 14 850

15 770 15 870

25 950 25 1250

50 1500 50 3000

80 2000 80 3950

Figure 5.12 show case the utilization of the system resource for the

number of tasks executed in both Hybrid with PCH and Hybrid Cloud. From

the graphical representation it is clear that the utilization usage of the

proposed system is less than that of the existing system and the X-axis

denotes Number of Task and Y-axis denotes the Utility Usage respectively.

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Figure 5.12 Utility of the system for different Tasks

5.4 SUMMARY

The main characteristics of virtualization technologies applied in

cloud environment are consolidating the resources, which will lead to

efficient management of resources. Here, two methods are addressed for

various optimized scheduling algorithms. The first method proposed, focused

on efficient utilization of resources, by using the parallel genetic algorithm for

Dynamic Deme model. This method investigates the scheduling procedure to

improve the utilization rate of the system resources. Due to this, the allotment

and releasing of the resources are done efficiently. The next method is used to

analyze the viability, from the view point of scalability, performance; cost of

deploying large virtual infrastructure, distributed over different cloud

providers for solving loosely coupled MTC. The performances of different

cluster configurations are evaluated with the performance metrics (cost

optimization, throughput, scalability and utility). Based on the evaluations,

the proposed method for resource scheduling is done very effectively.