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%.

翻译：随着计算密集型工作流在云环境中的日益普及，云平台在确保用户工作流调度满足严格截止期限和可靠性约束的同时优化能耗变得至关重要。云系统面临的核心挑战在于提供具有不同可靠性等级、能耗水平、处理频率和计算能力的虚拟机来执行这些工作流中的任务。为解决这些问题，我们提出一种基于最大扇出比的自适应策略，该策略综合考虑任务松弛时间与截止期限分布，用于在单一云平台中调度工作流，旨在保证严格可靠性与截止期限约束的同时最小化能耗。我们还提出一种基于滚动时域概念的动态工作流调度方法，以应对工作流任务执行时间的动态性——在多数情况下，运行时实际任务执行时间短于最坏情况执行时间。我们提出的静态方法在无截止期限约束场景下平均优于现有最佳方法达70%，在截止期限约束场景下实现约2%的性能提升。该方法的动态变体展现出更优异的性能：在无截止期限场景下超越现有最佳方法82%，在截止期限约束场景下平均提升达27%。此外，相较于静态最优解，我们的静态方法所得结果与最优解比值在1.1倍以内，而动态方法平均超越最优基线达25%。