AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Experimental results on SPEC CPU benchmarks show that MPORA delivers accurate predictions under unseen inputs and distribution shifts with low overhead, while improving schedulability and response times over existing methods.
PRISM, a prediction-guided runtime framework that jointly selects model variants and CPU allocations for containerized edge microservices, and adapts each pipeline stage in place and minimizes predicted CPU-package energy under deadline, resource, and offline model-level Quality of Result constraints is presented.
This paper introduces a job-level dynamic cache allocation strategy for multicore systems in which critical real-time tasks execute on a dedicated core while sharing the LLC with best-effort workloads executing on the remaining cores.
Fine-grained computation--communication overlap in distributed Mixture-of-Experts (MoE) inference allows communication to begin as partial compute results become ready. However, cooperative thread arrays (CTAs) performing computation and communication contend for finite residency capacity on streaming multiprocessors (...
Power-state management in high-performance computing (HPC) clusters must reduce idle energy without excessive wake-up delays for rigid parallel jobs. This paper presents SNF-ICON, an event-driven controller combining smallest-need-first (SNF) gang scheduling, predictive wake timing, and adaptive warm-spare control. At...
Apache Spark is widely used for distributed data processing, but accurately predicting application execution time remains challenging because performance depends on application structure, resource configuration, and executor‐allocation behavior. This article presents two deterministic, graph‐based simulation mode...
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