论文标题
CEDCE：多云系统中任务图的成本效益截止日期限制的进化调度程序
CEDCES: A Cost Effective Deadline Constrained Evolutionary Scheduler for Task Graphs in Multi-Cloud System
论文作者
论文摘要
许多科学工作流程可以建模为有向的无环图（此后称为DAG），其中节点代表单个任务，而有向的边缘表示两个任务之间的数据和控制流依赖关系。由于庞大的计算资源要求，单个云无法满足工作流程的要求。因此，是一个多云系统，其中多个云提供商将资源共同汇集成为一个很好的解决方案。在安排任务图中存在的任务时考虑的主要目标包括执行成本和makepan。在本文中，我们提出了成本效益的截止日期约束进化调度程序（此后称为CEDCES），旨在最大程度地减少给定截止日期约束下的执行成本。 CEDCE在其核心中包含基于粒子群优化的（此后称为PSO）方法，但是包括新颖的初始化，交叉和突变方案。对现实世界工作流的广泛模拟实验表明，CEDCE的表现优于最先进的算法，尤其是在执行成本方面平均为60.41％。在违反截止日期的情况下，CEDCE的执行时间最少，平均比其他人的表现胜过10.96％。
Many scientific workflows can be modeled as a Directed Acyclic Graph (henceforth mentioned as DAG) where the nodes represent individual tasks and the directed edges represent data and control flow dependency between two tasks. Due to large computational resource requirements, a single cloud cannot meet the requirements of the workflow. Hence, a multi-cloud system, where multiple cloud providers pool their resources together becomes a good solution. The major objectives considered while scheduling the tasks present in a task graph include execution cost and makespan. In this paper, we present Cost Effective Deadline Constrained Evolutionary Scheduler (henceforth mentioned as CEDCES) which aims to minimize the execution cost under a given deadline constraint. CEDCES contains Particle Swarm Optimization-based (henceforth mentioned as PSO) method in its core, however includes novel initialization, crossover, and mutation schemes. Extensive simulation experiments on real-world workflows show that CEDCES outperforms the state-of-art algorithms, in particular, 60.41% on average in terms of execution cost. In cases where the deadline is violated, CEDCES gives the least overshoot in execution time and outperforming the others by 10.96% on average.
