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

TL;DR

This paper introduces adaptive static and dynamic workflow scheduling strategies in cloud environments that significantly reduce energy consumption while satisfying strict deadlines and reliability constraints, outperforming existing methods.

Contribution

It proposes novel static and dynamic scheduling approaches based on maximum fan-out ratio and rolling horizon concepts, achieving substantial energy savings and improved performance over state-of-the-art techniques.

Findings

Static approach outperforms SOTA by up to 70% without deadlines.

Dynamic approach surpasses SOTA by 82% in non-deadline scenarios.

Static approach is within 1.1x of optimal; dynamic exceeds optimal by 25%.

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%.