Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds

Abstract

[EN] To meet the dynamic workload requirements in widespread task-batch based workflow applications, it is important to design algorithms for DAG-based platforms (such as Dryad, Spark and Pegasus) to rent virtual machines from public clouds dynamically. In terms of depths and functionalities, tasks of different task-batches are merged into task-units. A unit-aware deadline division method is investigated for properly dividing workflow deadlines to task deadlines so as to minimize the utilization of rented intervals. A rule-based task scheduling method is presented for allocating tasks to time slots of rented Virtual Machines (VMs) with a task right shifting operation and a weighted priority composite rule. A Unit-aware Rule-based Heuristic (URH) is proposed for elastically provisioning VMs to task-batch based workflows to minimize the rental cost in DAG-based cloud platforms. Effectiveness of the proposed URH methods is verified by comparing them against two adapted existing algorithms for similar problems on some realistic workflows.The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No.61602243 and 61572127), the Natural Science Foundation of Jiangsu Province (Grant No.BK20160846), the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No. 30916014107). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (DPI2015-65895-R) financed by FEDER funds.Cai, Z.; Li, X.; Ruiz García, R. (2019). Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds. IEEE Transactions on Cloud Computing. 7(3):814-826. https://doi.org/10.1109/TCC.2017.2663426S8148267