MLSysOps - Machine Learning for Autonomic System Operation in the Heterogeneous Edge-Cloud Continuum

The main objective of MLSysOps is to design, implement and evaluate a complete AI-controlled framework for autonomic end-to-end system management across the full cloud-edge continuum. MLSysOps will employ a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms of the continuum. Energy efficiency and utilization of green energy, performance, low latency, efficient, and trusted tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust, and security, are key elements of MLSysOps addressed using ML models.

Dynamic adaptivity of system configuration will be achieved through continual ML model learning in conjunction with intelligent retraining concurrently to application execution, while openness and extensibility will be supported through explainable ML methods and an API for pluggable ML models. Flexible/efficient application execution on heterogeneous infrastructures and nodes will be enabled through innovative portable container-based technology. The framework will be evaluated using research testbeds as well as two real-world application-specific testbeds in the domain of smart cities and smart agriculture, which will also be used to collect the system-level data necessary to train and validate the ML models, while realistic system simulators will be used to conduct scale-out experiments.

MLSysOps is not only fully aligned with current trends towards the expansion of cloud infrastructure towards integration with smart and deep edge resources, but it also achieves substantial research contributions in the realm of AI-based system adaptation across the cloud-edge continuum by introducing advanced methods and tools to enable optimal system management and application deployment. The MLSysOps consortium is a balanced blend of academic/research and industry/SME partners, bringing together the necessary scientific and technological skills to ensure successful implementation and impact.