Reliability aware scheduling of bag of real time tasks in cloud environment

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.