we need source code for load balancing in cloud computing

Introduction

The example demonstrates a communication scenario where the capacity of one running service is not enough and you want to increase the performance by distributing the workload across multiple services. To simulate the scenario, the example below implements a client application using a service to calculate π (PI) number. The calculation is split into many small intervals (400 in our example). Therefore, the service gets overloaded and the performance drops down.

The solution for such scenarios could be using of load balancer. The load balancer is a component maintaining a farm of services (typically same services but running on different machines) and is exposed to receive messages instead of the real service. The client does not know about this setup and thinks it communicates directly with the service. When the client sends a request, the load balancer receives this message and forwards it to the service picked from the farm. The service processes the message and sends back the response that is routed via the load balancer back to the client.

To run the example bellow you must execute applications in the following order:
1. PiCalculator up to 3 instances.
2. LoadBalancer
3. Client

ABSTRACT



Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more ef?cient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the ef?ciency in the public cloud environment.

Existing System:

Cloud computing is ef?cient and scalable but maintaining the stability of processing so many jobs in the cloud computing environment is a very complex problem with load balancing receiving much attention for researchers. Since the job arrival pattern is not predictable and the capacities of each node in the cloud differ, for load balancing problem, workload control is. crucial to improve system performance and maintain stability. Load balancing schemes depending on whether the system dynamics are important can be either static and dynamic . Static schemes do not use the system information and are less complex while dynamic schemes will bring additional costs for the system but can change as the system status changes. A dynamic scheme is used here for its ?exibility.

Disadvantages :

Load balancing schemes depending on whether the system dynamics are important can be either static and dynamic . Static schemes do not use the system information and are less complex.

Proposed System

Load balancing schemes depending on whether the system dynamics are important can be either static and dynamic . Static schemes do not use the system information and are less complex while dynamic schemes will bring additional costs for the system but can change as the system status changes. A dynamic scheme is used here for its ?exibility. The model has a main controller and balancers to gather and analyze the information. Thus, the dynamic control has

little in?uence on the other working nodes. The system status then provides a basis for choosing the right load balancing strategy.

The load balancing model given in this article is aimed at the public cloud which has numerous nodes with distributed computing resources in many different geographic locations. Thus, this model divides the public cloud into several cloud partitions. When the environment is very large and complex, these divisions simplify the load balancing. The cloud has a main controller that chooses the suitable partitions for arriving jobs while the balancer for each cloud partition

chooses the best load balancing strategy.



Implementation

Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective.

The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.

Main Modules:-

1. User Module :

In this module, Users are having authentication and security to access the detail which is presented in the ontology system. Before accessing or searching the details user should have the account in that otherwise they should register first.

2. System Model :

There are several cloud computing categories with this work focused on a public cloud. A public cloud is based on the standard cloud computing model, with service provided by a service provider . A large public cloud will include many nodes and the nodes in different geographical locations. Cloud partitioning is used to manage this large cloud. A cloud partition is a subarea of the public cloud with divisions based on the geographic locations. with the main controller deciding which cloud partition should receive the job. The partition load balancer then decides how to assign the jobs to the nodes. When the load status of a cloud partition is normal, this partitioning can be accomplished locally. If the cloud partition load status is not normal, this job should be transferred to another partition.

3. Main controller and balancers:

The load balance solution is done by the main controller and the balancers. The main controller ?rst assigns jobs to the suitable cloud partition and then communicates with the balancers in each partition to refresh this status information. Since the main controller deals with information for each partition, smaller data sets will lead to the higher processing rates. The balancers in each partition gather the status information from every node and then choose the right strategy to distribute the jobs.

4. Cloud Partition Load Balancing Strategy:

When the cloud partition is idle, many computing resources are available and relatively few jobs are arriving. In this situation, this cloud partition has the ability to process jobs as quickly as possible so a simple load balancing method can be used. There are many simple load balance algorithm methods such as the Random algorithm, the Weight Round Robin, and the Dynamic Round Robin the Round Robin algorithm is used here for its simplicity.