@Research Article 
#An Analytical Model for Dynamic Resource Allocation Framework in Cloud Environment#Kumar N. and Agarwal S.#1-6#1.ISCA-IVC-2014-05CITS-06.pdf#Cloud computing has emerged as the most popular paradigm for on-demand, pay-per-use model of computing. The software, platform and infrastructure as a service model will become the most popular mode of getting computing resources by common users. There has been growing research interest in managing the cloud of resources so as to achieve optimum utilization of resources along with desired quality of service. In the present scenario there is much scope of research in mapping users’ request to appropriate servers in cloud computing environment. In this paper, the authors propose an analytical model that maps dynamic users’ request to physical servers in the cloud that is based on a fixed charge mutli-index transportation problem. Thus a multi-index transportation Problem Cloud Resource Scheduler (MTPCRS) mechanism with mathematical formulation is developed along with a numerical example. A Multi- Indexed Cloud Resource Scheduling Algorithm (MICRSA) is also given in order to calculate the total cost of processing the service requests. With the help of sequence diagram and business process diagram it is shown that the model is simple to implement and produces an efficient and cost effective resource allocation plan for satisfying users’ requests. 
#QoS based Cloud Service Provider Selection Framework#Kumar N. and Agarwal S.#7-12#2.ISCA-IVC-2014-05CITS-07.pdf#A wide range of service offerings has been opened by the cloud computing industry to the customers. The cloud service providers and their services are increasing exponentially in present scenario of computing. In such a situation a customer faces the problem of selecting the best cloud service provider according to his personalized quality of service requirements. Moreover, the dynamic nature of the underlying network and several unpredictable circumstances, cause the service providers to depart from the promised QoS, as per Service Level Agreement. In this paper the authors present a framework for cloud service selection engine which acts as a tool to enable the customers to select the most appropriate cloud service provider from the Web Repository. The framework uses analytic hierarchy approach for multi-criteria QoS decision making which accelerates the selection process. Past users’ experience is used as a heuristic which helps the algorithm to converge in polynomial time. A comparison has been given between proposed mechanism and previous approaches and the results of proposed technique are promising.
#An Efficient Live VM Migration Technique in Clustered Datacenters#Kumar Narander and Saxena Swati#13-20#3.ISCA-IVC-2014-05CITS-08.pdf#The essence of cloud computing comes from the concept of virtualization. It is a technique of implementing a number of guest operating systems on a single host server such that memory and CPU resources of the host machine are shared among the guest systems. The guest machines are technically known as virtual machines (VMs). Virtualization enables full utilization of a physical machine in a cost-effective manner. However, varying and continuous load from users may overload a physical machine and this can lead to serious implications on performance, reliability and other service-level-agreement (SLA) parameters. To deal with this issue, VM migrations are practiced which enables the transfer of a virtual machine from an overloaded server to an under-loaded host, thereby relaxing the workload of the source host. Load balancing in a datacenter gives a chance to achieve a significantly high fault-tolerance, a feature which is very essential during live application executions in a cloud environment. This paper discusses the basic live migration techniques existing today, and proposes a hybrid approach of live VM migration, by combining the benefits of other existing techniques. Live migration refers to a transparent transfer of an active guest or virtual machine from a source server to a chosen destination server.