OPTIMIZED ENVIRONMENT ALLOCATION FOR STATIC AND DYNAMIC TASKS BASED ON THROTTLE ALGORITHM IN CLOUD

Cloud computing is a developing computing technology that has tend to every entity in the digital organization, it can be personal or government sector. Taking into account the significance of cloud computing, finding new ideas in advancements of cloud computing services is an area of research field. With the initiation of the Cloud, deployment and hosting became easier and cheaper with the use of pay-per-use model offered by Cloud providers. Usually clouds have powerful data centers and data controller to handle large number of users. Cloud is a platform providing dynamic pool of resources and virtualization of services. To properly manage the resources of the service contributor, load balancing is required for the jobs that are submitted to the data center controller. Load balancing is a technique to cut up workload across many virtual processing unit in a server over the network to achieve least data processing time, optimal resource utilization and least average response time. There are various load balancing algorithms that are round robin, connection least, active monitoring, equally spread current execution and throttle. In the existing work, the throttle load balancing approach distributes the incoming jobs uniformly among virtual machines based on its states whether it is busy or available. In this, all the virtual machines have same configuration in term of processing of task. There is no difference whether the task is static or dynamic. To overcome these, a new work is proposed which is based on appropriate allocation of virtual machines in terms of static and dynamic tasks and to find out the optimum cost for user as well as service provider. The proposed model is implemented and tested on simulation toolkit (CloudAnalyst). Results validate the correctness of the framework and show a significant improvement over existing work.