Performance analysis of resource scheduling and management in cloud infrastructures

Abstract

The demand of mobile and pervasive computing drastically leads an increased growth towards cloud computing and its services. Present day cloud environment suffers due to inefficient resource allocation. Inefficient resource handling and management leads too many hurdles which in turn makes the entire service to performance degradation. Furthermore, the selfdiscipline patterns in early cloud computing are applied in order to decrease resource request in resource processing and handling. Moreover, a tendency to present and compare architectures with the reference one that addresses the present basic approach, and talk about the models that use the multitenancy and desynchronizing in order to restrict cloud resource utilization. The study carried during this research has shown that the structures mishandling in resource allocation in regular cloud computing paradigms are downsizing the assets utilized. Modern technological need for high computation is also evidence to the growth of cloud oriented and assisted technologies. Present day IT deployments were mostly hosted in the cloud in order to minimize the costs of hardware, software etc. In order to handle huge data, data centers were deployed by the cloud service provider in the large scale. The data centers are responsible for data management, resource management and allocation. These data centers are considered to be hard coded electricity consumers which need high operational cost to handle its entire functionality. Resource management is a major problem in the data centers and requires majority of electricity in resource allocation. There is always a pressing need for an optimal and efficient resource allocation and scheduling technique. Hence in this research, a novel efficient resource allocation and scheduling technique using Hybrid Scheduling Queuing Model (HSQM) is proposed. The HSQM is optimal enough to decide when to allocate the resources or an assigned job that guarantee the resource allocation through the central server. Here the usage of the HSQM is