Cloud environment uses data center with a huge number of computational resources, and the probability of failing any of the resources increases with scale. Failures cause unavailability of services, which affects the reliability of the system. It is essential to consider the reliability issue for application deployment in the cloud, considering the failure of the resources. In this work, we address the reliability aware scheduling of tasks with hard deadlines in the cloud environment. We design, analyze and provide solutions for two special cases of the problem where (a) tasks have a common deadline on the machines with equal failure rate, and (b) tasks with equal execution time. For the general case of the problem, we propose two-phase heuristic approaches, one is the task ordering, and other is tasks mapping to machines. The performance of different task orderings and task mapping approaches is evaluated through simulation using synthetic and real traces. Based on the simulation result, the earliest due date ordering of tasks and mapping of the current task to the most reliable machine along with long task dropping performs better in general settings. We observe that task repetition and replication further improve the performance of the heuristics.

中文翻译：

---

云环境中实时任务包的可靠性感知调度

云环境使用具有大量计算资源的数据中心，并且任何资源失败的概率随着规模的扩大而增加。故障导致服务不可用，影响系统的可靠性。考虑到资源的故障，必须考虑云中应用程序部署的可靠性问题。在这项工作中，我们解决了云环境中具有硬期限的任务的可靠性感知调度。我们为问题的两种特殊情况设计、分析并提供解决方案，其中（a）任务在具有相同故障率的机器上具有共同的截止日期，以及（b）具有相同执行时间的任务。对于问题的一般情况，我们提出了两阶段启发式方法，一个是任务排序，另一个是任务映射到机器。不同任务排序和任务映射方法的性能是通过使用合成和真实轨迹的模拟来评估的。根据仿真结果，任务的最早截止日期排序和当前任务到最可靠机器的映射以及长时间的任务丢弃在一般设置中表现更好。我们观察到任务重复和复制进一步提高了启发式的性能。