4 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response
Yueying Li, Jiayang Chen, Yuanfan Chen +5
AI inference clusters are increasingly constrained by instantaneous power, not just energy: grid operators condition new capacity on demand response, imposing time-varying power ca…
A Performance Analyzer for a Public Cloud's ML-Augmented VM Allocator
Roozbeh Bostandoost, Pooria Namyar, Siva Kesava Reddy Kakarla +8
Cloud operators increasingly deploy multiple ML models in their VM allocation pipelines. In such settings, individually benign predictions can shift and compound, severely degradin…
EcoServe: Designing Carbon-Aware AI Inference Systems
Yueying Li, Zhanqiu Hu, Esha Choukse +3
The rapid increase in LLM ubiquity and scale levies unprecedented demands on computing infrastructure. These demands not only incur large compute and memory resources but also sign…
Towards Resource-Efficient Compound AI Systems
Gohar Irfan Chaudhry, Esha Choukse, Íñigo Goiri +3
Compound AI Systems, integrating multiple interacting components like models, retrievers, and external tools, have emerged as essential for addressing complex AI tasks. However, cu…
Aladdin: Joint Placement and Scaling for SLO-Aware LLM Serving
Chengyi Nie, Rodrigo Fonseca, Zhenhua Liu
The demand for large language model (LLM) inference is gradually dominating the artificial intelligence workloads. Therefore, there is an urgent need for cost-efficient inference s…
Junctiond: Extending FaaS Runtimes with Kernel-Bypass
Enrique Saurez, Joshua Fried, Gohar Irfan Chaudhry +5
This report explores the use of kernel-bypass networking in FaaS runtimes and demonstrates how using Junction, a novel kernel-bypass system, as the backend for executing components…