September 25 All day
The 1st International Workshop on Machine Learning for Autonomic System Operations in the Device-Edge-Cloud Continuum (MLSysOps 2023)
In conjunction with the International Conference on Embedded Wireless Systems and Networks (EWSN 2023), September 25, 2023, Rende, Italy.
To address the ever-increasing deluge of data collected and processed by computing systems, the trend of edge computing is establishing the effectiveness of processing data as close as possible to their source, often consisting of dense networks of wireless sensor nodes and IoT devices. It is predicted 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 devices with significant storage and processing capabilities. The advent of device-cloud-edge (D-E-C) computing aggravates the challenging task of managing heterogeneous and distributed resources, this time at an extreme scale, making human-in-the-loop management completely unrealistic. To achieve dynamic and flexible system and application management with minimal user involvement, the concept of autonomic computing systems was proposed as “computing systems that can manage themselves given high-level objectives from administrators”. However, the scale, heterogeneity, high dynamicity, and intrinsic local properties/variability of the continuum yields rule-based approaches – traditionally used in autonomic systems – insufficient. Machine learning-/AI-driven management is a promising alternative, but the quest to extend this to the full continuum faces several challenges such as scalability, heterogeneity, dynamics, trust & security, and transparency.
The goal of the workshop is therefore to bring together a community of researchers and practitioners who study problems at the intersection of AI/ML, autonomic and cognitive computing, D-E-C continuum, distributed system operation, and resilient application deployment.