QoS Aware Resource Utilization and Allocation in Cloud Computing

Abstract

Cloud computing provides resources on-demand on a rent basis through the internet. The cloud users request various services like computing power, storage, networking, etc, from the Cloud Service Providers (CSPs) on a rent basis. The demands of cloud users are increasing day by day because using the resources on rent is much easier and economical than purchasing a system with their own storage media, computing power, and network- ing etc. It is very challenging for CSPs to handle these service requests and manage the resources efficiently. Cloud computing has transformed the delivery of computational services to users as on-demand, customizable services, making them resource- and cost- effective. However, several obstacles prevent the widespread application of this technol- ogy, especially in educational institutions, central banks, and Cloud- Enterprize Resource Planning (C-ERP) etc. Other characteristics, such as on-demand service, resource pool- ing, pay-per-use, flexibility, etc., have enticed scientists to put scientific applications on the cloud. For successful exploitation of virtualized resources in the cloud, efficient re- source allocation based on task resource utilization is required to maximize performance and reduce execution time. Scientific Computing leverages cutting-edge, high-performance computing capabilities to handle complex problems in various scientific fields, such as weather forecasting, earth- quakes, subatomic particle behavior, turbulent flows, industrial processes, etc. As the resource requirements for resolving scientific problems are dynamic, there is a need for a platform capable of managing the data; as mentioned earlier, storage and processing limits in scientific applications. Further different scientific applications are categorized on the basis of their basic shape, size and structure which can be deployed on the cloud envi- ronment. These applications are further classified based on their computational runtime and task dependencies.