LAUSR

Cloud Infrastructure as a Service (IaaS) has been known as a suitable platform for the execution of workflow applications. Quality of service (QoS) in such platforms is considered a challenging problem from both customers’ and service providers’ perspectives to perform workflow schedules. This paper proposes Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC) algorithms to consider both budget and deadline constraints for scheduling scientific workflows on cloud IaaS platforms. Methods for distribution of budget and deadlines under task leveling are proposed. Four metrics (success rate, time ratio, cost ratio, and utilization rate) are utilized to evaluate the proposed algorithms’ performance. Results of our proposed algorithms are compared with the BDHEFT, DBCS, and BDSD algorithms under various scenarios. Simulation results demonstrate that BDDC outperforms other algorithms in achieving cheaper costs while earning a higher success rate and utilization rate, and BDC accomplishes higher success rates and faster makespan. The performance of the proposed methods is confirmed using a real cloud environment.