Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain Internet of Things based framework
Osama Almurshed, Ashish Kumar Kaushal, Souham Meshoul, Asmail Muftah, Osama Almoghamis, Ioan Petri, Nitin Auluck, OMER F. RANA · Future Generation Computer Systems · 2024
The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for sectors such as smart agriculture, industry, and healthcare, EO-GNH integrates asynchronous MapReduce and parallel meta-heuristics to effectively manage AI applications, focusing on execution performance, resource utilization, and infrastructure resilience. The framework carefully addresses the distribution challenges of AI applications, especially Service Function Chains (SFCs), in edge-cloud infrastructures. It contains Data Flow Management, which covers aspects of data storage and data privacy, and also considers factors like regional adaptations, mobile access, and AI model refinement. EO-GNH ensures high availability for forecasting, prediction, and training AI models, operating efficiently within a geo-distributed infrastructure. The proposed strategies within EO-GNH emphasize concurrent multi-node execution, enhancing AI application placement by improving execution time, dependability, and cost-effectiveness. The efficiency of EO-GNH is demonstrated through its impact on QoS in real-time resource management across three application domains, highlighting its adaptability and potential in diverse cross-domain IoT-based environments. • EO-GNH optimizes AI application placement in edge computing environments for improved QoS performance • EO-GNH leverages Parsl for parallel meta-heuristic execution and optimized workload deployment • EO-GNH is faster than distributed NSGA-II in finding solutions to multi-objective optimization problems • EO-GNH delivers edge AI capabilities from federated learning to real-time inference • EO-GNH transforms IoT operations across healthcare, industry, and agriculture applications