Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds

TLDR

Cloud computing offers opportunities for large‑scale scientific problems but presents challenges for workflow scheduling, and existing approaches often fail to meet QoS requirements or exploit cloud elasticity and heterogeneity. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on IaaS clouds. The strategy employs a particle swarm optimization algorithm to minimize execution cost while satisfying deadline constraints, and is evaluated with CloudSim on various scientific workflows. Results show that the approach outperforms current state‑of‑the‑art algorithms.

Abstract

Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.