A flexible deadline-driven resource provisioning and scheduling algorithm for multiple workflows with VM sharing protocol on WaaS-cloud

Rajasekar, P.; Yogesh, P.

doi:10.1007/s11227-021-04225-1

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…Compute intensive applications such as scientific workflows believe in dynamic scheduling as their compute requirements do change with time. This is witnessed by many techniques and studies proposed in recent years [23][24][25][26][27][28]. Scheduling algorithms could use heuristic and metaheuristic approaches and, therefore, two well-known categories of the heuristic and meta-heuristic algorithms do exist in the literature.…”

Section: Workflow Scheduling Mechanismsmentioning

confidence: 99%

“…The latter technique (presented in [24]) used divide and conquer approach to achieve deadline constrained cost optimization. A flexible deadline driven resource management algorithm for multiple workflows is presented in [26]. Similarly, a bi-objective multi-workflow cloud scheduling mechanism based on re-enforcement learning is presented in [27] while an energy aware multi-workflow scheduling for hybrid clouds is presented in [28].…”

Section: Workflow Scheduling Mechanismsmentioning

confidence: 99%

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A Unified Mechanism for Cloud Scheduling of Scientific Workflows

et al. 2022

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Scheduling plays a vital role in the efficient utilization of the available resources in clouds. This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows. These investigations used four different sizes of workloads each, for, five well-known workflows. It was revealed that none of the existing algorithms is capable of efficiently executing all the four sizes of workload for the complete set of workflows. Different algorithms performed better, when they were applied to various workloads of a particular workflow. This fact was used in developing an improved unified mechanism, which is capable of using an existing algorithm that performed well in the past, against the given workload. Evaluation results showed that the proposed mechanism improved over the existing algorithms for 4 out of 5 workflows (Epigenomics, Inspiral, Cyber Shake, and Montage), when tested against an aggregated load of all sizes, in terms of simulation time. For the workflow named SIPHT, however, it responded exactly the same as Max-Min algorithm. The minimum and maximum improvements, against the existing best and worst algorithms, in percentage, for Epigenomics, Inspiral, SIPHT, Cyber Shake and Montage were 16-63, 30-68, 0-69, 30-68, and 9-71 in corresponding order. This work has an additional overhead in terms of a dedicated module to find and store algorithmic performance. It is, however, required once and, thus, the increase in execution time might be marginal. The future work intends to check the impact of compute time towards optimization parameters such as makespan, pricing and deadlines.

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