Utshab Saha, Avijit Podder, Hillol Dhar, Shubrahjoity Paul

Under the guidance of: Prof. Santanu DamGROUP NO.:- 5

Topics

Introduction

Introduction

• Principles followed by Cloud Computing.• Resource Pooling.• Virtualization.• Elasticity.• Automatic resource deployment.• Metered Billing.

Cloud

Actual

resources

Background

BackgroundCloud component

• Cloud client platforms.• Thick client.• Thin client.• Zero client.(Ultra thin)

Cloud server

Thin clientThin client

Intern

et

Thick clientThick clientInternet

BackgroundCloud component

• Cloud Storage• Public cloud.• Private cloud.• Community cloud.• Hybrid cloud.

Public cloud

R1

R2

R3

Shared resourceShared resource

UB 1

UB 2

UB 3

UB 4

Private cloud

R1

R2

R3

UB 1/Org 1UB 2/Org 1

Private resourcePrivate resource

BackgroundCloud component

• Cloud Network• High bandwidth(low latency).• Agile network.• Network security.

Background

Background

• Deployment.• Public cloud.• Private cloud.• Community cloud.• Hybrid cloud.

Public

Background

• Deployment.• Public cloud.• Private cloud.• Community cloud.• Hybrid cloud.

Private

Background

• Deployment.• Public cloud.• Private cloud.• Community cloud.• Hybrid cloud.

Community

Background

• Deployment.• Public cloud.• Private cloud.• Community cloud.• Hybrid cloud.

Hybrid

Background

• Load balancing.• Type of load balancing.• Static.• Dynamic.

• Need of load balancing.• Improving the performance.• Maintaining the system stability.• Quality of services.(QoS)• Building fault tolerance.

Algorithm Survey from several Literature

• Proposed several load balancing algorithms• Scheduling algorithms.• Round Robin.• FCFS.

• Soft computing based algorithms.• Stochastic algorithm.[1]• Genetic algorithm.[2]• Ant colony optimization algorithm.[3]

Algorithm Survey from several Literature

From: Dam santanu et. Al. 2015 C3IT

Proposed work

• Our proposed algorithm.• VM allocation optimization using Simulated annealing.

• Host Side optimization.

• Target.• Balancing load of the virtual nodes and reducing Response Time(RT).

• Progress with the project.• Using Cloud Sim simulated Data Centers, Virtual Machines, Cloudlets.

• Virtually distributed the load.

Proposed work

• Progress with project.

Proposed work

• Our next move.• Migrating to Cloud Analyst and implementing our

proposed algorithm.

OUR WORK

• The two algorithms implemented are 1) ROUND ROBIN. 2)FCFS. 3)SIMULATED ANNEALING

• Round robin is the scheduling algorithm used by the CPU during execution of the process.• All processes in this algorithm are kept in the circular

queue also known as ready queue.• By using this algorithm, CPU makes sure, time slices

( any natural number ) are assigned  to each process in equal portions and in circular order

IMPLEMENTING ROUND ROBIN

public int getNextAvailableVm(){        currVm++;                if (currVm >= vmStatesList.size()){            currVm = 0;        }                allocatedVm(currVm);                return currVm;            }

RESULT OF ROUND ROBIN ALGORITHM

RESULT OF ROUND ROBIN ALGORITHM

First Come First Serve

• First come, first served (FCFS) is an operating system process scheduling algorithm and a network routing management mechanism.•With first come, first served, what comes first is handled

first.• The next request in line will be executed once the one

before it is complete.

IMPLEMENTING FCFS

public int getNextAvailableVm() {                int temp=-1;        if(vmStatesList.size()>0) {            for (Iterator<Integer> itr = vmStatesList.keySet().iterator(); itr.hasNext();) {                                temp = itr.next();                VirtualMachineState state = vmStatesList.get(temp);                if(state.equals(VirtualMachineState.AVAILABLE)){                    allocatedVm(temp);                    break;                }            }        }        return temp;            }

RESULT OF FCFS ALGORITM

RESULT OF FCFS ALGORITM

Simulated Annealing

• Simulated annealing (SA) is a probabilistic technique.• VMs are assigned to have probability which tells availability of

VMs.• Then using function call we checked highest probability and

selected the VM.• Accordingly decremented the probability.

Implementation of Simulated Annealing

•Probability Data Structure static float[][] anArrayOfFloats = new float[2][999999]; //probability array

•Implementationprivate float getHighProbability(){

float high_probability = anArrayOfFloats[1][0];for(int i = 1; i<vmStatesList.size(); i++) {if(high_probability<anArrayOfFloats[1][i])

high_probability = anArrayOfFloats[1][i];}return high_probability;

}

Result of Simulated Annealing

COMPARISON STUDY

Algorithms

Over All Response Time Data Center Processing Time

Avg(ms) Min(ms) Max(ms) Avg(ms) Min(ms) Max(ms)Round Robin

300.06 237.06 369.12 0.34 0.02 0.61

FCFS 300.09 237.06 369.12 0.38 0.08 4.5Simulated Annealing

297.87 271.61 346.62 0.48 0.10 0.61

Checks best Host

according to

Fitness

VM requested for Host

VM queueHost 1Host 2

.

.

.Host n

Host Side Optimization

Data center

Host information

Request for Host Info

Best Host

Request for Host

Request for Host

Best Host

Our proposed algorithm to optimize Host selection is Genetic Algorithm

References

[1] Avani Kansara, Ronak Patel et al. “A Various Load Balancing Techniques and Challenges in Cloud Computing – Survey ” International Journal for Scientific Research & Development Vol. 2, Issue 10, 2014 ISSN (online): 2321-0613 [2] Dasgupta, Kousik et al. "A Genetic Algorithm (GA) Based Load Balancing Strategy For Cloud Computing". Procedia Technology 10 (2013): 340-347. Web.[3] Santanu Dam 1, Gopa Mandal2, Kousik Dasgupta3, Paramartha Dutta4 et al. “An Ant Colony Based Load Balancing Strategy in Cloud Computing “. Advanced Computing, Networking and Informatics  Volume 2 Smart Innovation, Systems and Technologies Volume 28, 2014, pp 403 413