The field of cloud-edge computing is moving towards more scalable, efficient, and resilient architectures. Researchers are exploring novel approaches to enhance Quality of Service (QoS) in edge computing frameworks, such as federated layering techniques and collaborative state machines. These innovations enable better management of dynamic and stateful applications, improved reasoning and decision-making processes, and enhanced security and privacy. Noteworthy papers include: Collaborative State Machines: A Better Programming Model for the Cloud-Edge-IoT Continuum, which introduces a programming model that facilitates the development of reactive, event-driven, and stateful applications. Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems, which proposes a novel approach to enhance QoS through federated layering techniques and model layering with privacy protection measures.
Cloud-Edge Computing Advancements
Sources
Offloading tracing for real-time systems using a scalable cloud infrastructure
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems
Collaborative State Machines: A Better Programming Model for the Cloud-Edge-IoT Continuum
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML