Abstract
The cloud being the latest technology, provides immense opportunities to solve large-scale scientific problems. This computing paradigm’s success enables the IaaS provider to offer infinite resources based on the pay-per-use model. Although the workflow scheduling problem has been widely investigated, however, most of them are concerned with a single QoS constraint and ignores the consideration of multiple QoS constrained problems. Therefore, this paper proposes a novel strategy of dynamic resource provisioning and scheduling approach for deadline-budget constrained workflows (DRP-DBAS). The algorithm intends to minimize makespan while subject to deadline and budget constraints for the hourly-based cost model of the IaaS cloud. For resource provisioning, the number of instances leased is based on the budget available. For the scheduling, the HEFT algorithm has been extended with the deadline and budget constraint. Further, the new scheduling approach incorporates the clustering mechanism to cluster the pipelined task, which reduces the overall execution time and enhances the algorithm’s performance. The DRP-DBAS is compared against the existing BDSD, BDAS, and GRP-HEFT and the obtained result proves the efficacy of the DRP-DBAS algorithm. DRP-DBAS outperforms the other algorithm by achieving a PSR of 56.11%, followed by GRP-HEFT with PSR 44.61%, BDAS with PSR 39.13%, and BDSD with PSR 26.13%.
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Rizvi, N., Ramesh, D. (2021). DRP-DBAS: Dynamic Resource Provisioning for Deadline and Budget Aware Workflow Scheduling in IaaS Clouds. In: Venugopal, K.R., Shenoy, P.D., Buyya, R., Patnaik, L.M., Iyengar, S.S. (eds) Data Science and Computational Intelligence. ICInPro 2021. Communications in Computer and Information Science, vol 1483. Springer, Cham. https://doi.org/10.1007/978-3-030-91244-4_13
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