arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.
By Satyandra Guthula, Jaber Daneshamooz, Charles Fleming, Kesheng Wu, Walter Willinger, Arpit Gupta
arXiv:2606. 09787v1 Announce Type: new Abstract: The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential.
By Abd Elghani Meliani, Arora Sagar, Adlen Ksentini, Raymond Knopp
arXiv:2609.35760v2 Announce Type: replace-cross
Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The age...
By Chaoqian Ouyang, Ling Yue, Libin Zheng, Hanghui Guo, Shengxiang Xu, YiShu Wang, Ran Li, Jian Yin, Shaowu Pan, Shimin Di
arXiv:2606. 03910v1 Announce Type: cross Abstract: Disaggregated LLM inference forces the KV cache to traverse the datacenter network before decoding begins, so transfer time enters directly into the Time to First Token (TTFT) budget.
By Mubarak Adetunji Ojewale
arXiv:2607. 06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the complexity of coordinating massive client populations.
By Dhruv Garg, Neha Lakhani, Debopam Sanyal, Myungjin Lee, Alexey Tumanov, Ada Gavrilovska
The paper investigates how enterprise wireless access points (APs) can run predictive machine learning models while sharing CPU and memory with essential networking tasks. It introduces the concept of network‑aware deployability, requiring models to qualify for the AP’s hardware and to be validated under real packet‑service constraints. Benchmarks reveal that models can run 6–19× slower and use up to 22% more memory on an AP compared to a Raspberry Pi 5, and that even similarly sized forecasting models can differ by 19× in latency, leading to significant increases in round‑trip time and throughput degradation under load.
By Niloo Bahadori, Swadhin Pradhan, Peiman Amini