arXiv:2608. 11840v1 Announce Type: cross Abstract: Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand.
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arXiv:2606. 03061v1 Announce Type: cross Abstract: Emerging distributed computing paradigms, such as the computing continuum, are inherently heterogeneous, stochastic, and complex.
arXiv:2607. 26602v1 Announce Type: cross Abstract: The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications.
arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.
arXiv:2606. 11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features.
arXiv:2608. 02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints.
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.