5. Resource Provisioning Strategy for Scientific Workflows in Cloud Computing Environment

Cloud computing has emerged as a computing paradigm to solve large-scale problems. The main intent of Cloud computing is to provide inexpensive computing resources on a pay-as-you-go basis, which is promptly gaining momentum as a substitute for traditional information technology (IT)-based organizations. Therefore, the increased utilization of Clouds makes successful execution of scientific applications a vital research area. As more and more users have started to store and process their real-time data in Cloud environments, resource provisioning and scheduling of huge Data processing jobs becomes a key element of consideration for efficient execution of scientific applications. The base of any real-time system is a resource, and to manage the resources to handle workflow applications in Cloud computing environment is a very tedious task. An inefficient resource management system can have a direct negative effect on performance and cost and indirect effect on functionality of the system. Indeed, some functions provided by the system may become too expensive or may be avoided due to poor performance. Thus, Cloud computing faces the challenge of resource management, especially with respect to choosing resource provisioning strategies and suitable algorithms for particular applications. The major components of resource management systems are resource provisioning and scheduling. If any system is able to fulfill the requirements of these two components, the execution of scientific workflow applications will become much easier. This chapter discusses the fundamental concepts supporting Cloud computing and resource management system terms and the relationship between them. It reflects the essential perceptions behind the Cloud resource provisioning strategies. The chapter also identifies requirements based on user’s applications associated with handling real-time data. A model for resource provisioning based on user’s requirements to maximize efficiency and analysis of scientific workflows is also proposed. QoS parameter (s) based resource provisioning strategy has been proposed for workflow applications in cloud computing environment. Validation of resource provisioning strategies is presented in this book chapter.