Real-time workflows oriented online scheduling in uncertain cloud environment
Real-time workflows oriented online scheduling in uncertain cloud environment
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DOI：
10.1007/s11227-017-2060-4
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发表时间：
2017-11-01
影响因子：
3.3
通讯作者：
Shen, Xin
中科院分区：
文献类型：
--
作者：
Chen, Huangke;Zhu, Jianghan;Shen, Xin
工作流调度已成为云环境中最热门的话题之一，高效的调度方法显示了通过最小化成本来最大化云提供商利润的有前途的方法，同时保证用户应用程序的服务质量。然而，现有的调度方法不足以适应在云环境中运行的任务执行时间不确定的动态工作流，因为这些方法假设云计算环境是确定性的，并且在调度执行期间将静态地遵循预先计算的调度决策。为了解决上述问题，我们引入了一种不确定性感知调度架构，以减轻不确定因素对工作流调度质量的影响。基于该架构，我们提出了一种调度算法，结合了事件驱动和周期性滚动策略（EDPRS），用于调度动态工作流。最后，我们进行了大量的实验，使用真实工作流程跟踪将 EDPRS 与两种典型的基线算法进行比较。实验结果表明，EDPRS 的性能优于这些算法。
Workflow scheduling has become one of the hottest topics in cloud environments, and efficient scheduling approaches show promising ways to maximize the profit of cloud providers via minimizing their cost, while guaranteeing the QoS for users' applications. However, existing scheduling approaches are inadequate for dynamic workflows with uncertain task execution times running in cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. To cover the above issue, we introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertain factors on the workflow scheduling quality. Based on this architecture, we present a scheduling algorithm, incorporating both event-driven and periodic rolling strategies (EDPRS), for scheduling dynamic workflows. Lastly, we conduct extensive experiments to compare EDPRS with two typical baseline algorithms using real-world workflow traces. The experimental results show that EDPRS performs better than those algorithms.