Serverless Workflows for Containerised Applications in the Cloud Continuum

This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.

An Osmosis-Based Intelligent Agent Scheduling Framework for Cloud Bursting in a Hybrid Cloud

An intelligent system to efficiently provision resources in a hybrid cloud environment is necessary due to the high level of complexity. The semi-permeable agent for hybrid cloud scheduling (SPAH) is a bio-inspired agent that adapts the biological process of osmosis into cloud bursting. The primary objective of the agent is to minimize the makespan. The framework and algorithm for the two phases of SPAH, to recognize the state and decide on action are presented. A QoS (Quality of Service) deadline factor metric is proposed to study the indirect impact of SPAH in deadline satisfaction. SPAH shows significant improvement in deadline satisfaction of up to 85% as compared to other cloud bursting techniques. This is the result of a reduced makespan and a reduced cumulative waiting time. The analysis of SPAH shows that it works in quadratic time complexity.