Multi-Objective Workflow Scheduling to Serverless Architecture in a Multi-Cloud Environment

Many complex workflows consist of multiple tasks represented as a directed acyclic graph (DAG). Optimal deployment of such workflows on a cloud using multiple services requires a judicious selection of compute and storage services to minimize the makespan and the cost of deployment. Multi-cloud deployment is emerging as a preferred choice for the deployment of complex workflows for price competitiveness and freedom from vendor lock-in. However, finding an optimal mapping scheme for heterogeneous tasks of a workflow in a multi-cloud environment is a challenge. Furthermore, each participating cloud service provider (CSP) has a unique cost model and maximum deliverable performance. This makes exploration of the optimal configuration of the chosen service daunting. Many algorithms, frameworks, and tools have been proposed to schedule complex workflows on cloud using virtual machines (VMs) available as Infrastructure-as-a-Service (IaaS). However, the use of scalable and cost-effective serverless platforms offered as Function-as-a-Service (FaaS) is still in its infancy. In this work, we use particle swarm optimization (PSO) for mapping complex workflows to cloud services such as computing, and storage in a multi-cloud scenario. We map complex workflows to the serverless platforms and storage services from popular cloud vendors namely Amazon Web Services (AWS), Azure (AZR), and Google Cloud Platform (GCP). The experimental evaluation shows that our approach results in up to 61% improvement in makespan and 51% improvement in the cost of workflow deployment as compared to naive and intuition-based mapping in a cloud.