Variability-Aware Latency Amelioration in Distributed Environments. - Alexey Tumanov; Robert S. Allison; Wolfgang Stürzlinger (VR, 2007)

Kaleidoscope: cloud micro-elasticity via VM state coloring. - Roy Bryant; Alexey Tumanov; Olga Irzak; Adin Scannell; Kaustubh R. Joshi; Matti A. Hiltunen; H. Andrés Lagar-Cavilla; Eyal de Lara (EuroSys, 2011)

Heterogeneity and dynamicity of clouds at scale: Google trace analysis. - Charles Reiss; Alexey Tumanov; Gregory R. Ganger; Randy H. Katz; Michael A. Kozuch (SoCC, 2012)

alsched: algebraic scheduling of mixed workloads in heterogeneous clouds. - Alexey Tumanov; James Cipar; Gregory R. Ganger; Michael A. Kozuch (SoCC, 2012)

Exploiting iterative-ness for parallel ML computations. - Henggang Cui; Alexey Tumanov; Jinliang Wei; Lianghong Xu; Wei Dai 003; Jesse Haber-Kucharsky; Qirong Ho; Gregory R. Ganger; Phillip B. Gibbons; Garth A. Gibson; Eric P. Xing (SoCC, 2014)

PriorityMeister: Tail Latency QoS for Shared Networked Storage. - Timothy Zhu; Alexey Tumanov; Michael A. Kozuch; Mor Harchol-Balter; Gregory R. Ganger (SoCC, 2014)

SpringFS: bridging agility and performance in elastic distributed storage. - Lianghong Xu; James Cipar; Elie Krevat; Alexey Tumanov; Nitin Gupta 001; Michael A. Kozuch; Gregory R. Ganger (FAST, 2014)

Agility and Performance in Elastic Distributed Storage. - Lianghong Xu; James Cipar; Elie Krevat; Alexey Tumanov; Nitin Gupta 001; Michael A. Kozuch; Gregory R. Ganger (ACM Trans. Storage, 2014)

TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters. - Alexey Tumanov; Timothy Zhu; Jun Woo Park; Michael A. Kozuch; Mor Harchol-Balter; Gregory R. Ganger (EuroSys, 2016)

Morpheus: Towards Automated SLOs for Enterprise Clusters. - Sangeetha Abdu Jyothi; Carlo Curino; Ishai Menache; Shravan Matthur Narayanamurthy; Alexey Tumanov; Jonathan Yaniv; Ruslan Mavlyutov; Iñigo Goiri; Subru Krishnan; Janardhan Kulkarni; Sriram Rao (OSDI, 2016)

Scheduling with Space-Time Soft Constraints In Heterogeneous Cloud Datacenters. - Alexey Tumanov (2016)

Proteus: agile ML elasticity through tiered reliability in dynamic resource markets. - Aaron Harlap; Alexey Tumanov; Andrew Chung; Gregory R. Ganger; Phillip B. Gibbons (EuroSys, 2017)

Real-Time Machine Learning: The Missing Pieces. - Robert Nishihara; Philipp Moritz; Stephanie Wang; Alexey Tumanov; William Paul; Johann Schleier-Smith; Richard Liaw; Mehrdad Niknami; Michael I. Jordan; Ion Stoica (HotOS, 2017)

IDK Cascades: Fast Deep Learning by Learning not to Overthink. - Xin Wang 066; Yujia Luo; Daniel Crankshaw; Alexey Tumanov; Joseph E. Gonzalez (CoRR, 2017)

Ray: A Distributed Framework for Emerging AI Applications. - Philipp Moritz; Robert Nishihara; Stephanie Wang; Alexey Tumanov; Richard Liaw; Eric Liang; William Paul; Michael I. Jordan; Ion Stoica (CoRR, 2017)

3Sigma: distribution-based cluster scheduling for runtime uncertainty. - Jun Woo Park; Alexey Tumanov; Angela H. Jiang; Michael A. Kozuch; Gregory R. Ganger (EuroSys, 2018)

Ray: A Distributed Framework for Emerging AI Applications. - Philipp Moritz; Robert Nishihara; Stephanie Wang; Alexey Tumanov; Richard Liaw; Eric Liang; Melih Elibol; Zongheng Yang; William Paul; Michael I. Jordan; Ion Stoica (OSDI, 2018)

IDK Cascades: Fast Deep Learning by Learning not to Overthink. - Xin Wang 066; Yujia Luo; Daniel Crankshaw; Alexey Tumanov; Fisher Yu 001; Joseph E. Gonzalez (UAI, 2018)

Tributary: spot-dancing for elastic services with latency SLOs. - Aaron Harlap; Andrew Chung; Alexey Tumanov; Gregory R. Ganger; Phillip B. Gibbons (USENIX ATC, 2018)

InferLine: ML Inference Pipeline Composition Framework. - Daniel Crankshaw; Gur-Eyal Sela; Corey Zumar; Xiangxi Mo; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (CoRR, 2018)

Serverless Computing: One Step Forward, Two Steps Back. - Joseph M. Hellerstein; Jose M. Faleiro; Joseph Gonzalez 001; Johann Schleier-Smith; Vikram Sreekanti; Alexey Tumanov; Chenggang Wu 001 (CIDR, 2019)

Cirrus: a Serverless Framework for End-to-end ML Workflows. - João Carreira; Pedro Fonseca 001; Alexey Tumanov; Andrew Zhang; Randy H. Katz (SoCC, 2019)

HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline. - Richard Liaw; Romil Bhardwaj; Lisa Dunlap; Yitian Zou; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (SoCC, 2019)

Lineage stash: fault tolerance off the critical path. - Stephanie Wang; John Liagouris; Robert Nishihara; Philipp Moritz; Ujval Misra; Alexey Tumanov; Ion Stoica (SOSP, 2019)

Dynamic Space-Time Scheduling for GPU Inference. - Paras Jain 001; Xiangxi Mo; Ajay Jain; Harikaran Subbaraj; Rehan Sohail Durrani; Alexey Tumanov; Joseph Gonzalez 001; Ion Stoica (CoRR, 2019)

The OoO VLIW JIT Compiler for GPU Inference. - Paras Jain 001; Xiangxi Mo; Ajay Jain; Alexey Tumanov; Joseph E. Gonzalez; Ion Stoica (CoRR, 2019)

InferLine: latency-aware provisioning and scaling for prediction serving pipelines. - Daniel Crankshaw; Gur-Eyal Sela; Xiangxi Mo; Corey Zumar; Ion Stoica; Joseph Gonzalez 001; Alexey Tumanov (SoCC, 2020)

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units. - Shenda Hong; Yanbo Xu; Alind Khare; Satria Priambada; Kevin O. Maher; Alaa Aljiffry; Jimeng Sun 001; Alexey Tumanov (KDD, 2020)

Cloudburst: Stateful Functions-as-a-Service. - Vikram Sreekanti; Chenggang Wu 001; Xiayue Charles Lin; Johann Schleier-Smith; Jose M. Faleiro; Joseph E. Gonzalez; Joseph M. Hellerstein; Alexey Tumanov (CoRR, 2020)

Cloudburst: Stateful Functions-as-a-Service. - Vikram Sreekanti; Chenggang Wu 001; Xiayue Charles Lin; Johann Schleier-Smith; Joseph Gonzalez 001; Joseph M. Hellerstein; Alexey Tumanov (Proc. VLDB Endow., 2020)

RubberBand: cloud-based hyperparameter tuning. - Ujval Misra; Richard Liaw; Lisa Dunlap; Romil Bhardwaj; Kirthevasan Kandasamy; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (EuroSys, 2021)

CompOFA - Compound Once-For-All Networks for Faster Multi-Platform Deployment. - Manas Sahni; Shreya Varshini; Alind Khare; Alexey Tumanov (ICLR, 2021)

ESCHER: expressive scheduling with ephemeral resources. - Romil Bhardwaj; Alexey Tumanov; Stephanie Wang; Richard Liaw; Philipp Moritz; Robert Nishihara; Ion Stoica (SoCC, 2022)

Automatic Parallelization of Python Programs for Distributed Heterogeneous Computing. - Jun Shirako; Akihiro Hayashi; Sri Raj Paul; Alexey Tumanov; Vivek Sarkar (Euro-Par, 2022)

CoDG-ReRAM: An Algorithm-Hardware Co-design to Accelerate Semi-Structured GNNs on ReRAM. - Yixuan Luo; Payman Behnam; Kiran Thorat; Zhuo Liu; Hongwu Peng; Shaoyi Huang; Shu Zhou; Omer Khan; Alexey Tumanov; Caiwen Ding; Tong Geng (ICCD, 2022)

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. - Yanbo Xu; Alind Khare; Glenn Matlin; Monish Ramadoss; Rishikesan Kamaleswaran; Chao Zhang; Alexey Tumanov (NeurIPS, 2022)

TransEHR: Self-Supervised Transformer for Clinical Time Series Data. - Yanbo Xu; Shangqing Xu; Manav Ramprassad; Alexey Tumanov; Chao Zhang 014 (ML4H@NeurIPS, 2023)

Subgraph Stationary Hardware-Software Inference Co-Design. - Payman Behnam; Alexey Tumanov; Tushar Krishna; Pranav Gadikar; Yangyu Chen; Jianming Tong; Yue Pan; Abhimanyu Rajeshkumar Bambhaniya; Alind Khare (MLSys, 2023)

SuperFed: Weight Shared Federated Learning. - Alind Khare; Animesh Agrawal; Myungjin Lee; Alexey Tumanov (CoRR, 2023)

DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. - Amey Agrawal; Sameer Reddy; Satwik Bhattamishra; Venkata Prabhakara Sarath Nookala; Vidushi Vashishth; Kexin Rong 001; Alexey Tumanov (CoRR, 2023)

Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems. - Debopam Sanyal; Jui-Tse Hung; Manav Agrawal; Prahlad Jasti; Shahab Nikkhoo; Somesh Jha; Tianhao Wang 001; Sibin Mohan; Alexey Tumanov (CoRR, 2023)

ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation. - Anshul Ahluwalia; Rohit Das; Payman Behnam; Alind Khare; Pan Li 005; Alexey Tumanov (CoRR, 2023)

Signed Binarization: Unlocking Efficiency Through Repetition-Sparsity Trade-Off. - Sachit Kuhar; Yash Jain; Alexey Tumanov (CoRR, 2023)

Hardware-Software Co-Design for Real-Time Latency-Accuracy Navigation in Tiny Machine Learning Applications. - Payman Behnam; Jianming Tong; Alind Khare; Yangyu Chen; Yue Pan; Pranav Gadikar; Abhimanyu Bambhaniya; Tushar Krishna; Alexey Tumanov (IEEE Micro, 2023)

Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. - Amey Agrawal; Sameer Reddy; Satwik Bhattamishra; Venkata Prabhakara Sarath Nookala; Vidushi Vashishth; Kexin Rong 001; Alexey Tumanov (SoCC, 2024)

DεpS: Delayed ε-Shrinking for Faster Once-for-All Training. - Aditya Annavajjala; Alind Khare; Animesh Agrawal; Igor Fedorov; Hugo Latapie; Myungjin Lee; Alexey Tumanov (ECCV (89), 2024)

SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. - Alind Khare; Animesh Agrawal; Aditya Annavajjala; Payman Behnam; Myungjin Lee; Hugo Latapie; Alexey Tumanov (ECCV (79), 2024)

Harmonica: Hybrid Accelerator to Overcome Imperfections of Mixed-signal DNN Accelerators. - Payman Behnam; Uday Kamal; Ali Shafiee; Alexey Tumanov; Saibal Mukhopadhyay (IPDPS, 2024)

VIDUR: A Large-Scale Simulation Framework for LLM Inference. - Amey Agrawal; Nitin Kedia; Jayashree Mohan; Ashish Panwar; Nipun Kwatra; Bhargav S. Gulavani; Ramachandran Ramjee; Alexey Tumanov (MLSys, 2024)

Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve. - Amey Agrawal; Nitin Kedia; Ashish Panwar; Jayashree Mohan; Nipun Kwatra; Bhargav S. Gulavani; Alexey Tumanov; Ramachandran Ramjee (OSDI, 2024)

DεS: Delayed ε-Shrinking for Faster Once-For-All Training. - Aditya Annavajjala; Alind Khare; Animesh Agrawal; Igor Fedorov; Hugo Latapie; Myungjin Lee; Alexey Tumanov (CoRR, 2024)

Metron: Holistic Performance Evaluation Framework for LLM Inference Systems. - Amey Agrawal; Anmol Agarwal; Nitin Kedia; Jayashree Mohan; Souvik Kundu 009; Nipun Kwatra; Ramachandran Ramjee; Alexey Tumanov (CoRR, 2024)

Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations. - Amey Agrawal; Junda Chen; Íñigo Goiri; Ramachandran Ramjee; Chaojie Zhang; Alexey Tumanov; Esha Choukse (CoRR, 2024)

Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning. - Kaustubh Ponkshe; Raghav Singhal; Eduard Gorbunov; Alexey Tumanov; Samuel Horváth; Praneeth Vepakomma (CoRR, 2024)

PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off. - Sachit Kuhar; Yash Jain; Alexey Tumanov (Trans. Mach. Learn. Res., 2024)

Client Availability in Federated Learning: It Matters! - Dhruv Garg; Debopam Sanyal; Myungjin Lee; Alexey Tumanov; Ada Gavrilovska (EuroMLSys, 2025)

SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads. - Alind Khare; Dhruv Garg; Sukrit Kalra; Snigdha Grandhi; Ion Stoica; Alexey Tumanov (NSDI, 2025)

∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS. - Payman Behnam; Uday Kamal; Sanjana Vijay Ganesh; Zhaoyi Li; Michael Jurado; Alind Khare; Igor Fedorov; Gaowen Liu; Alexey Tumanov (Trans. Mach. Learn. Res., 2025)

RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression. - Payman Behnam; Yaosheng Fu; Ritchie Zhao; Po-An Tsai; Zhiding Yu; Alexey Tumanov (CoRR, 2025)

Maya: Optimizing Deep Learning Training Workloads using Emulated Virtual Accelerators. - Srihas Yarlagadda; Amey Agrawal; Elton Pinto; Hakesh Darapaneni; Mitali Meratwal; Shivam Mittal; Pranavi Bajjuri; Srinivas Sridharan; Alexey Tumanov (CoRR, 2025)

On Evaluating Performance of LLM Inference Serving Systems. - Amey Agrawal; Nitin Kedia; Anmol Agarwal; Jayashree Mohan; Nipun Kwatra; Souvik Kundu 009; Ramachandran Ramjee; Alexey Tumanov (CoRR, 2025)

Efficient LLM Inference via Chunked Prefills. - Arney Agrawal; Nitin Kedia; Ashish Panwar; Jayashree Mohan; Nipun Kwatra; Bhargav S. Gulavani; Alexey Tumanov; Ramachandran Ramjee (ACM SIGOPS Oper. Syst. Rev., 2025)

EMPIRIC: Exploring Missing Pieces in KV Cache Compression for Reducing Computation, Storage, and Latency in Long-Context LLM Inference. - Payman Behnam; Yaosheng Fu; Ritchie Zhao; Po-An Tsai; Zhiding Yu; Alexey Tumanov (ACM SIGOPS Oper. Syst. Rev., 2025)

Toward Weight Sharing Paradigm for Efficient AI: Training and Inference Serving. - Payman Behnam; Alind Khare; Dhruv Garg; Alexey Tumanov (ACM SIGOPS Oper. Syst. Rev., 2025)