Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Muhammad Hafizhuddin Hilman, Maria A Rodriguez, Rajkumar Buyya, A Sill, J Spillner
2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) | IEEE | Published : 2018
Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profilin..View full abstract
This research is partially supported by LPDP (Indonesia Endowment Fund for Education) and ARC (Australia Research Council) research grant.