Description

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. In general, scheduling is the process of mapping tasks to available resources on the basis of tasks’ characteristics and requirements. It is an important aspect in efficient working of cloud as various task parameters need to be taken into account for appropriate scheduling. The available resources should be utilized efficiently without affecting the service parameters of cloud. Scheduling process in cloud can be generalized into three stages namely– Resource discovering and filtering – Datacenter Broker discovers the resources present in the network system and collects status information related to them. Resource selection – Target resource is selected based on certain parameters of task and resource. This is deciding stage. Task submission -Task is submitted to resource selected. 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 and prioritized. Resource selection is done on the basis of task constraints using a greedy approach. The proposed model is implemented on VMware tool. Results validate the correctness of the framework and show a significant improvement over sequential scheduling.