Minimizing Energy in Reliability and Deadline-Ensured Workflow Scheduling in Cloud
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918030332657664 |
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| author | Sarkar, Suvarthi V, Dhanesh Singh, Ketan Sahu, Aryabartta |
| author_facet | Sarkar, Suvarthi V, Dhanesh Singh, Ketan Sahu, Aryabartta |
| contents | With the increasing prevalence of computationally intensive workflows in cloud environments, it has become crucial for cloud platforms to optimize energy consumption while ensuring the feasibility of user workflow schedules with respect to strict deadlines and reliability constraints. The key challenges faced when cloud systems provide virtual machines of varying levels of reliability, energy consumption, processing frequencies, and computing capabilities to execute tasks of these workflows. To address these issues, we propose an adaptive strategy based on maximum fan-out ratio considering the slack of tasks and deadline distribution for scheduling workflows in a single cloud platform, intending to minimise energy consumption while ensuring strict reliability and deadline constraints. We also propose an approach for dynamic scheduling of workflow using the rolling horizon concept to consider the dynamic execution time of tasks of the workflow where the actual task execution time at run time is shorter than worst-case execution time in most of the cases. Our proposed static approach outperforms the state-of-the-art (SOTA) by up to 70% on average in scenarios without deadline constraints, and achieves an improvement of approximately 2% in deadline-constrained cases. The dynamic variant of our approach demonstrates even stronger performance, surpassing SOTA by 82% in non-deadline scenarios and by up to 27% on average when deadline constraints are enforced. Furthermore, in comparison with the static optimal solution, our static approach yields results within a factor of 1.1, while the dynamic approach surpasses the optimal baseline by an average of 25%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_16496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minimizing Energy in Reliability and Deadline-Ensured Workflow Scheduling in Cloud Sarkar, Suvarthi V, Dhanesh Singh, Ketan Sahu, Aryabartta Distributed, Parallel, and Cluster Computing With the increasing prevalence of computationally intensive workflows in cloud environments, it has become crucial for cloud platforms to optimize energy consumption while ensuring the feasibility of user workflow schedules with respect to strict deadlines and reliability constraints. The key challenges faced when cloud systems provide virtual machines of varying levels of reliability, energy consumption, processing frequencies, and computing capabilities to execute tasks of these workflows. To address these issues, we propose an adaptive strategy based on maximum fan-out ratio considering the slack of tasks and deadline distribution for scheduling workflows in a single cloud platform, intending to minimise energy consumption while ensuring strict reliability and deadline constraints. We also propose an approach for dynamic scheduling of workflow using the rolling horizon concept to consider the dynamic execution time of tasks of the workflow where the actual task execution time at run time is shorter than worst-case execution time in most of the cases. Our proposed static approach outperforms the state-of-the-art (SOTA) by up to 70% on average in scenarios without deadline constraints, and achieves an improvement of approximately 2% in deadline-constrained cases. The dynamic variant of our approach demonstrates even stronger performance, surpassing SOTA by 82% in non-deadline scenarios and by up to 27% on average when deadline constraints are enforced. Furthermore, in comparison with the static optimal solution, our static approach yields results within a factor of 1.1, while the dynamic approach surpasses the optimal baseline by an average of 25%. |
| title | Minimizing Energy in Reliability and Deadline-Ensured Workflow Scheduling in Cloud |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.16496 |