Autonomous Choreography of WebAssembly Workloads in the Federated Cloud-Edge-IoT Continuum

TL;DR

This paper introduces a comprehensive architecture for autonomous workload management in the federated Cloud-Edge-IoT continuum, leveraging AI and open standards to address heterogeneity and multi-stakeholder challenges.

Contribution

It proposes a novel, multi-layered architecture for choreographing workloads across the continuum, integrating AI techniques and open standards for autonomous resource management.

Findings

Architectural framework for continuum-native applications

Use of AI for autonomous workload orchestration

Identification of future research directions

Abstract

The concept of the federated Cloud-Edge-IoT continuum promises to alleviate many woes of current systems, improving resource use, energy efficiency, quality of service, and more. However, this continuum is still far from being realized in practice, with no comprehensive solutions for developing, deploying, and managing continuum-native applications. Breakthrough innovations and novel system architectures are needed to cope with the ever-increasing heterogeneity and the multi-stakeholder nature of computing resources. This work proposes a novel architecture for choreographing workloads in the continuum, attempting to address these challenges. The architecture that tackles this issue comprehensively, spanning from the workloads themselves, through networking and data exchange, up to the orchestration and choreography mechanisms. The concept emphasizes the use of varied AI techniques, enabling autonomous and intelligent management of resources and workloads. Open standards are also a key part of the proposition, making it possible to fully engage third parties in multi-stakeholder scenarios. Although the presented architecture is promising, much work is required to realize it in practice. To this end, the key directions for future research are outlined.