مهلت برنامه ریزی محدود کردن جریان های علمی در منابع ابری ارائه شده به صورت پویا

The work in this article concentrates on the problem of scheduling deadline constrained scientific workloads on dynamically provisioned cloud resources, while reducing the cost of computation. Specifically we present two algorithms, Proportional Deadline Constrained (PDC) and Deadline Constrained Critical Path (DCCP) that address the workflow scheduling problem on such dynamically provisioned cloud resources. These algorithms are additionally extended to refine their operation in task prioritization and backfilling respectively. The results in this article indicate that both PDC and DCCP algorithms achieve higher cost efficiencies and success rates when compared to existing algorithms.