Minimizing Energy in Reliability and Deadline-Ensured Workflow Scheduling in Cloud

2025-05-22

AI Summary

This paper addresses energy-efficient scheduling of compute-intensive workflows in cloud environments under strict deadline and reliability constraints. We propose a static-dynamic协同 virtual machine scheduling framework: (1) In the static phase, we design an adaptive scheduling strategy leveraging maximum fan-out ratio and task slackness; (2) In the dynamic phase, we introduce a novel online adjustment mechanism integrating receding horizon control (RHC) to react in real time to execution-time deviations. Our deadline-aware energy-efficiency model jointly optimizes energy consumption while guaranteeing both temporal deadlines and reliability requirements. Experimental results demonstrate that the static scheduler achieves 2% energy savings over state-of-the-art (SOTA) methods under deadline constraints—and up to 70% savings when deadlines are relaxed. The dynamic scheduler further improves energy efficiency by up to 82% (deadline-relaxed) or 27% (deadline-constrained), outperforming the static optimal solution by 25% in overall performance.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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%.

Problem

• Minimize energy in cloud workflow scheduling
• Ensure strict deadline and reliability constraints
• Optimize dynamic task execution time handling

Innovation

• Adaptive strategy using maximum fan-out ratio
• Dynamic scheduling with rolling horizon concept
• Minimizes energy while ensuring reliability and deadlines