Energy-efficient workflow scheduling based on workflow structures under deadline and budget constraints in the cloud

TL;DR

This paper introduces two novel workflow scheduling algorithms for cloud environments that optimize energy efficiency and resource utilization while respecting deadlines and budgets, leveraging workflow structure properties.

Contribution

The paper presents two structure-aware scheduling algorithms, SMWSO and SMWSH, that improve energy efficiency and resource allocation in cloud workflows, including heterogeneous VM environments.

Findings

80% of scenarios see improved energy efficiency

Over 50% energy savings compared to recent algorithms

Effective handling of workflow structures enhances scheduling performance

Abstract

The utilization of cloud environments to deploy scientific workflow applications is an emerging trend in scientific community. In this area, the main issue is the scheduling of workflows, which is known as an NP-complete problem. Apart from respecting user-defined deadline and budget, energy consumption is a major concern for cloud providers in implementing the scheduling strategy. The types and the number of virtual machines (VMs) used are determinant to handle those issues, and their determination is highly influenced by the structure of the workflow. In this paper, we propose two workflow scheduling algorithms that take advantage of the structural properties of the workflows. The first algorithm is called Structure-based Multi-objective Workflow Scheduling with an Optimal instance type (SMWSO). It introduces a new approach to determine the optimal instance type along with the optimal number of VMs to be provisioned. We also consider the use of heterogeneous VMs in the Structure-based Multi-objective Workflow Scheduling with Heterogeneous instance types (SMWSH), to highlight the algorithm's strength within the heterogeneous environment. The simulation results show that our proposal produces better energy-efficiency in 80% of workflow/workload scenarios, and save more than 50% overall energy compared to a recent state-of-the-art algorithm.