Compensatory Aggregation based Failure Aware Cloud Workflow Scheduling

Scientific and business application workflows modeled as directed acyclic graphs represent the complex computational problems, which are to be executed in distributed computing environments. Due to sharing, elasticity, complexity, heterogeneity, virtualization, and workload variations, the cloud computing systems offer a host of challenges in terms of performance and reliability. While running a complex workflow application in the IaaS cloud, failures may be triggered by its resource failures or workflow level failures, which lead to large-scale service disruption with adverse consequences. In some static task scheduling algorithms like earliest completion time scheduling (ECTS) and heterogeneous earliest finish time (HEFT), since resource availability and the probability of task completion are not considered, workflow execution delay increases makespan. The proposed compensatory aggregation based failure aware (CAFA) cloud workflow scheduling algorithm considers resource selection based on the availability influenced by CPU load and processor speed for good performance and the probability of task completion for reliability. A method of compensatory aggregation of criteria is used for scoring each resource and choosing the resource with the highest score. The performance of CAFA algorithm is much better in terms of reduced makespan compared to HEFT and ECTS algorithms and is also found to be more reliable.