Abstract
Cloud Computing has gained high attention by provisioning resources and software as a service. Throughout the years, the number of users of clouds is increasing and thus will increase the number of tasks and load in the cloud. Therefore, scheduling tasks efficiently and dynamically is a critical problem to be solved. There are many scheduling algorithms that are used in cloud computing but most of them are concentrating on minimizing time and cost and some of them concentrate on increasing fault tolerance. However, very few scheduling algorithms that considers time, cost, and fault tolerance at the same time. Moreover, Considering pricing models in developing scheduling algorithms to provide cost-effective fault tolerant techniques is still in its infancy. Therefore, analysing the impact of the different pricing models on scheduling algorithm will lead to choosing the right pricing model that will not affect the cost. This paper proposes developing a scheduling algorithm that combines these features to provide an efficient mapping of tasks and improve Quality of Service (QoS).