Serverless Workflows for Containerised Applications in the Cloud Continuum

Abstract

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.

FaaScinating Resilience for Serverless Function Choreographies in Federated Clouds

“…However, both HyperFlow and MPSC may run an FC on a single FaaS system region at a time, thereby are restricted to the limitations of that FaaS system. Risco et al [18] introduced OSCAR, a programming language agnostic serverless framework, which offloads functions to a single cloud region when the edge resources are overloaded. However, OSCAR neither considers the concurrency limitations of the FaaS providers nor can it schedule complex FCs with data and control flow dependencies.…”

IoT Serverless Computing at the Edge: A Systematic Mapping Review

“…Real world examples are already available in this case as well [24,25]. The data processing does not need to take place exclusively on serverless platform in the cloud, and instead can be migrated to the edge as well, optimizing bandwidth usage should the computing resources meet the required performance [26].…”

“…However, by offering easy-to-use interfaces, and integration with the cloud, it is possible to leverage the proximity of the edge not only for reduced latency, but also for increased privacy, to preprocess data that would ultimately be analyzed and aggregated in the cloud. This is especially useful for research studies that gather various sensor data containing personally identifiable information, which needs to be anonymized first [26]. A persistent issue faced by all serverless edge platforms is how to connect with the end-users and end-devices who would invoke the available functions.…”

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

“…The moniker 'public' does not always imply that it is free, even if it is possible to utilize it for free or at a low cost. A public cloud does not imply that a user's data is publicly accessible; public cloud companies often provide means for their users to manage access to their own data [15].…”

“…The moniker 'public' does not always imply that it is free, even if it is possibl utilize it for free or at a low cost. A public cloud does not imply that a user's data is p licly accessible; public cloud companies often provide means for their users to man access to their own data [15]. Figure 3 depicts a private cloud that is created for a specific group or organization and restricts access to that group or organization.…”