Fair budget constrained workflow scheduling approach for heterogeneous clouds
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Fair budget constrained workflow scheduling approach for heterogeneous clouds Naela Rizvi1 • Dharavath Ramesh1 Received: 4 February 2019 / Revised: 16 February 2020 / Accepted: 24 February 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The phenomenal advancement of technology paved the way for the execution of complex scientific applications. The emergence of the cloud provides a distributed heterogeneous environment for the execution of large and complex workflows. Due to the dynamic and heterogeneous nature of the cloud, scheduling workflows become a challenging problem. Mapping and assignment of heterogeneous instances for each task while minimizing execution time and cost is a NP-complete problem. For efficient scheduling, it is required to consider various QoS parameters such as time, cost, security, and reliability. Among these, computation time and cost are the two notable parameters. In order to preserve the functionalities of these two parameters in heterogeneous cloud environments, in this paper, a fair budget-constrained workflow scheduling algorithm (FBCWS) is proposed. The novelty of the proposed algorithm is to minimize the makespan while satisfying budget constraints and a fair means of schedule for every task. FBCWS also provides a mechanism to save budget by adjusting the cost-time efficient factor of the minimization problem. The inclusion of a cost-time efficient factor in the algorithm provides flexibility to minimize the makespan or save budget. In order to validate the effectiveness of the proposed approach, several real scientific workflows are simulated, and experimental results are compared with other existing approaches, namely; Heterogeneous Budget Constrained Scheduling (HBCS), Minimizing Schedule Length using Budget Level (MSBL) and Pareto Optimal Scheduling Heuristic (POSH) algorithms. Experimental results prove that the proposed algorithm behaves outstandingly for compute-intensive workflows such as Epigenomic and Sipht. Also, FBCWS outperforms the existing HBCS in most of the cases. Moreover, FBCWS proves to be more time-efficient than POSH and more cost-efficient than MSBL. The effectiveness of the proposed algorithm is illustrated through the popular ANOVA test. Keywords Workflow scheduling DAG Budget constraints Makespan Heterogeneous clouds
1 Introduction Workflows have been widely used to model complex scientific and business applications. The evolution of these complex scientific applications with an urge of large scale computing and storage services demanded the design of a high-performance computing system. Therefore, the & Dharavath Ramesh [email protected] Naela Rizvi [email protected] 1
Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand 826004, India
distributed and heterogeneous environments, like grids and clusters, have gained momentum in terms of computational instances to execute these complex scientific a
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