A Dynamic Optimization Algorithm for Task Scheduling in Cloud Environment

Cloud computing has emerged as a popular computing model to support on demand services. It is a style of computing where massively scalable resources are delivered as a service to external customers using Internet technologies. Scheduling in cloud is responsible for selection of best suitable resources for task execution, by taking some static and dynamic parameters and restrictions of tasks’ into consideration. The users’ perspective of efficient scheduling may be based on parameters like task completion time or task execution cost etc. Service providers like to ensure that resources are utilized efficiently and to their best capacity so that resource potential is not left unused. This paper proposes a scheduling algorithm which addresses these major challenges of task scheduling in cloud. The incoming tasks are grouped on the basis of task requirement like minimum execution time or minimum cost and prioritized. Resource selection is done on the basis of task constraints using a greedy approach. The proposed model is implemented and tested on simulation toolkit. Results validate the correctness of the framework and show a significant improvement over sequential scheduling.