To tame the ever-increasing deluge of data collected and processed by modern computing systems, there is a strong trend towards processing data as close as possible to their sources, known as edge computing. It is expected that by 2025 around 80% of enterprise data will be generated and processed outside the traditional cloud. In fact, edge computing is becoming even more attractive with the advent of energy-efficient micro-servers and powerful embedded IoT devices with significant storage and processing capabilities.
However, the rise of cloud-edge-IoT systems and the plethora of different computing and sensing devices that are involved in modern applications further aggravates the challenging task of monitoring and managing heterogeneous and distributed resources, this time at an extreme scale, making human-in-the-loop management completely unrealistic. The solution is to make computing systems “autonomic” so that they can manage themselves based on high-level objectives from the application owners and system administrators.
The goal of the MLSysOps project is to support autonomic system management across the cloud-edge-IoT continuum, using a combination of AI and ML methods. MLSysOps will develop a hierarchical agent framework on top of different resource management and application deployment/orchestration mechanisms. To achieve adaptivity, the agents will incorporate continual ML model learning in tandem with intelligent retraining during application execution. The project emphasizes openness and extensibility, by dissociating management from control and employing explainable ML methods and an API for pluggable ML models. Energy efficiency and utilization of green energy, performance, low latency, efficient tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust, and security, are key aspects we intend to address in the project.
More specifically, the MLSysOps project has the following key objectives: (1) Deliver an open AI-ready, agent-based framework for holistic, trustworthy, scalable, and adaptive system operation across the heterogeneous cloud-edge continuum. (2) Develop an AI architecture supporting explainable, efficiently retrainable ML models for end-to-end autonomic system operation in the cloud-edge continuum. (3) Enable efficient, flexible, and isolated execution across the heterogeneous continuum. (4) Support green, resource-efficient, and trustworthy system operation, while satisfying application QoS/QoE requirements. (5) Perform realistic model training, validation, and evaluation.
The technology that will be developed in the MLSysOps project will be tested and evaluated using several research testbeds and two real-world application testbeds in the domain of smart cities and smart agriculture. Additionally, system simulators will enable scale-out experiments that cannot be performed using the available testbeds.
