Edge Computing Advancements

The field of edge computing is rapidly advancing, with a focus on improving real-time processing, reducing latency, and increasing efficiency. Researchers are exploring innovative architectures and algorithms to optimize edge computing systems, including the use of machine learning, graph neural networks, and distributed hierarchical models. These advancements have the potential to revolutionize various applications, such as smart grid optimization, intelligent buildings, and large-scale distributed systems. Notably, the development of adaptive co-inference frameworks, sparse spatiotemporal models, and instruction-based coordination architectures is pushing the boundaries of edge computing capabilities.

Some noteworthy papers in this area include: LAD-BNet, which achieves 14.49% MAPE at 1-hour horizon with only 18ms inference time on Edge TPU. ACE-GNN, which achieves a speedup of up to 12.7x and an energy savings of 82.3% compared to GCoDE. ECCENTRIC, which reduces computation and communication costs while achieving the best performance possible. Distributed Hierarchical Machine Learning, which produces near-optimal allocations with low inference time and maintains permutation equivariance over variable-size device sets. Instruction-Based Coordination, which enables programmable multi-PU synchronization and achieves notable compute efficiency and throughput efficiency gains. A Hybrid Proactive And Predictive Framework, which combines time series forecasting and multi-agent Deep Reinforcement Learning to make better decisions about resource management.

Sources

LAD-BNet: Lag-Aware Dual-Branch Networks for Real-Time Energy Forecasting on Edge Devices

ACE-GNN: Adaptive GNN Co-Inference with System-Aware Scheduling in Dynamic Edge Environments

Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review

ECCENTRIC: Edge-Cloud Collaboration Framework for Distributed Inference Using Knowledge Adaptation

A Meta-Heuristic Load Balancer for Cloud Computing Systems

How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems

Distributed Hierarchical Machine Learning for Joint Resource Allocation and Slice Selection in In-Network Edge Systems

SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction

Instruction-Based Coordination of Heterogeneous Processing Units for Acceleration of DNN Inference

A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management

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