Edge-Cloud Computing
Overview
Edge-cloud computing studies how to place and coordinate computation across devices, edge servers, and remote clouds, especially when applications need fast responses or cannot send all data away. Common approaches include task offloading and DNN partitioning, reinforcement-learning schedulers, adaptive model routing, and compression or semantic communication that sends compact features instead of raw data. These methods support settings such as IoT monitoring, vehicles, healthcare, video analysis, and distributed language-model inference, where latency, bandwidth, energy, privacy, and accuracy must be balanced. A central challenge is keeping service quality predictable as networks, workloads, and device capacity change, while validating proposed strategies on realistic hardware and under strict latency and reliability targets.
Latest papers
67 papers7 papers in the last four weeks, down 36% on the four weeks before. 0.0% of all new papers.