Neeraja J. Yadwadkar

I am a post-doctoral researcher in the Computer Science Department at Stanford University. My research interests are in Cloud Computing, Computer Systems, and Machine Learning. I graduated with a PhD in Computer Science from University of California, Berkeley. My dissertation was on Automatic Resource Management in the Datacenter and the Cloud. I received my masters in Computer Science from the Indian Institute of Science, Bangalore, India.

Most of my research straddles the boundaries of systems, and Machine Learning (ML). Advances in Systems, Machine Learning (ML), and hardware architectures are about to launch a new era in which we can use the entire cloud as a computer. New ML techniques are being developed for solving complex resource management problems in systems. Similarly, systems research is getting influenced by properties of emerging ML algorithms, and evolving hardware architectures. Bridging these complementary fields, my research focuses on using and developing ML techniques for systems, and building systems for ML.

Email:

Publications

[NEW] Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
Francisco Romero*, Mark Zhao*, Neeraja J. Yadwadkar, and Christos Kozyrakis
(in submission)

ArXiv Preprint
[NEW] SmartHarvest: Harvesting Idle CPUs Safely and Efficiently in the Cloud
Yawen Wang, Kapil Arya, Marios Kogias, Manohar Vanga, Aditya Bhandari, Neeraja J. Yadwadkar, Siddhartha Sen, Sameh Elnikety, Christos Kozyrakis, and Ricardo Bianchini
(to appear in EuroSys 2021)
[NEW] Practical Scheduling for Real-World Serverless Computing
Kostis Kaffes, Neeraja J. Yadwadkar, and Christos Kozyrakis
(revision for EuroSys 2021)
[NEW] What Serverless Computing Is and Should Become: The Next Phase of Cloud Computing
Johann Schleier-Smith, Vikram Sreekanti, Anurag Khandelwal, Joao Carreira, Neeraja J. Yadwadkar, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica, and David A. Patterson
(contributed article to appear in Communications of the ACM)
INFaaS: A Model-less Inference Serving System
Francisco Romero*, Qian Li*, Neeraja J. Yadwadkar, and Christos Kozyrakis
(in submission)

ArXiv Preprint BibTeX Website

@misc{infaas,
title={INFaaS: A Model-less and Managed Inference Serving System},
author={Francisco Romero and Qian Li and Neeraja J. Yadwadkar and Christos Kozyrakis},
year={2020},
eprint={1905.13348},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
Centralized Core-granular Scheduling for Serverless Functions
Kostis Kaffes, Neeraja J. Yadwadkar, and Christos Kozyrakis
The ACM Symposium on Cloud Computing 2019 (SoCC), November 2019

Program BibTeX

@article{serverless_scheduling_socc19,
author = {Kostis Kaffes and Neeraja J. Yadwadkar and Christos Kozyrakis},
title = {Centralized Core-granular Scheduling for Serverless Functions},
journal = {SoCC},
year = {2019},
}
A Case for Managed and Model-less Inference Serving
Neeraja J. Yadwadkar, Francisco Romero, Qian Li, and Christos Kozyrakis
The 17th Workshop on Hot Topics in Operating Systems (HotOS), May 2019

PDF BibTeX Website Showcased in 'the morning paper'

@inproceedings{model-less_hotos,
author = {Neeraja J. Yadwadkar and Francisco Romero and Qian Li and Christos Kozyrakis},
title = {A Case for Managed and Model-less Inference Serving},
booktitle = {Proceedings of the Workshop on Hot Topics in Operating Systems},
series = {HotOS '19},
year = {2019},
isbn = {978-1-4503-6727-1},
location = {Bertinoro, Italy},
pages = {184--191},
numpages = {8},
url = {http://doi.acm.org/10.1145/3317550.3321443},
doi = {10.1145/3317550.3321443},
acmid = {3321443},
publisher = {ACM},
keywords = {Automatic Resource Management, Inference Serving, Model-less},
}
Cloud Programming Simplified: A Berkeley View on Serverless Computing
Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, Joseph E Gonzalez, Raluca Ada Popa, Ion Stoica, David A Patterson
EECS Department, University of California, Berkeley Technical Report, February 2019

PDF BibTeX

@techreport{bv_serverless_techreport,
author = {Eric Jonas and Johann Schleier-Smith and Vikram Sreekanti and Chia-Che Tsai and Anurag Khandelwal and Qifan Pu and Vaishaal Shankar and Joao Menezes Carreira and Karl Krauth and Neeraja J. Yadwadkar and Joseph E. Gonzalez and Raluca Ada Popa and Ion Stoica and David A. Patterson},
bdsk-url-1 = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html},
institution = {EECS Department, University of California, Berkeley},
keywords = {techreport},
month = {2},
number = {UCB/EECS-2019-3},
title = {Cloud Programming Simplified: A Berkeley View on Serverless Computing},
url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.html},
year = {2019}
}
Context: The Missing Piece in the Machine Learning Lifecycle
Rolando Garcia, Vikram Sreekanti, Neeraja J. Yadwadkar, Daniel Crankshaw, Joseph E. Gonzalez, Joseph M. Hellerstein
Workshop on Common Model Infrastructure at KDD, 2018

PDF BibTeX

@inproceedings{context_cmi,
author = {Rolando Garcia and Vikram Sreekanti and Neeraja J. Yadwadkar and Daniel Crankshaw and Joseph E. Gonzalez and Joseph M. Hellerstein},
bdsk-url-1 = {http://www.vikrams.io/papers/flor-cmi18.pdf},
booktitle = {Proceedings of the KDD Workshop on Common Model Infrastructure (CMI)},
keywords = {peerrev},
month = {8},
title = {Context: The Missing Piece in the Machine Learning Lifecycle},
url = {http://www.vikrams.io/papers/flor-cmi18.pdf},
year = {2018}
}
Selecting the Best VM across Multiple Public Clouds: A Data-Driven Performance Modeling Approach
Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Burton Smith, and Randy Katz
ACM Symposium on Cloud Computing (SoCC), 2017

PDF BibTeX Demo

@inproceedings{paris,
author = {Neeraja J. Yadwadkar and Bharath Hariharan and Joseph E. Gonzalez and Burton Smith and Randy H. Katz},
title = {Selecting the Best VM Across Multiple Public Clouds: A Data-driven Performance Modeling Approach},
booktitle = {Proceedings of the 2017 Symposium on Cloud Computing},
series = {SoCC '17},
year = {2017},
isbn = {978-1-4503-5028-0},
location = {Santa Clara, California},
pages = {452--465},
numpages = {14},
url = {http://doi.acm.org/10.1145/3127479.3131614},
doi = {10.1145/3127479.3131614},
acmid = {3131614},
publisher = {ACM},
keywords = {cloud computing, data-driven modeling, performance prediction, resource allocation},
}
Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, and Randy Katz
Journal of Machine Learning Research (JMLR), 2016

PDF BibTeX

@article{JMLR:v17:15-149,
author = {Neeraja J. Yadwadkar and Bharath Hariharan and Joseph E. Gonzalez and Randy H. Katz},
title = {Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling},
journal = {Journal of Machine Learning Research},
year = {2016},
volume = {17},
number = {106},
pages = {1-37},
url = {http://jmlr.org/papers/v17/15-149.html}
}
Faster Jobs in Distributed Data Processing using Multi-Task Learning
Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, and Randy Katz
SIAM International Conference on Data Mining (SDM), 2015

PDF BibTeX Video

@inbook{faster-jobs-mtl,
author = {Neeraja J. Yadwadkar and Bharath Hariharan and Joseph E. Gonzalez and Randy H. Katz},
year = {2015},
month = {06},
pages = {532-540},
title = {Faster Jobs in Distributed Data Processing using Multi-Task Learning},
isbn = {978-1-61197-401-0},
doi = {10.1137/1.9781611974010.60}
}
Wrangler: Predictable and Faster Jobs using Fewer Resources
Neeraja J. Yadwadkar, Ganesh Ananthanarayanan, and Randy Katz
ACM Symposium on Cloud Computing (SoCC), 2014

PDF BibTeX Slides

@inproceedings{wrangler,
author = {Neeraja J. Yadwadkar and Ganesh Ananthanarayanan and Joseph E. Gonzalez and Randy H. Katz},
title = {Wrangler: Predictable and Faster Jobs Using Fewer Resources},
booktitle = {Proceedings of the ACM Symposium on Cloud Computing},
series = {SOCC '14},
year = {2014},
location = {Seattle, WA, USA},
pages = {26:1--26:14},
articleno = {26},
numpages = {14},
acmid = {2671005},
publisher = {ACM},
}
Discovery of Application Workloads from Network File Traces
Neeraja J. Yadwadkar, Chiranjib Bhattacharyya, K. Gopinath, Thirumale Niranjan, and Sai Susarla
Usenix Conference on File and Storage Technologies (FAST), 2010

PDF BibTeX Video

@inproceedings{Yadwadkar:2010:DAW:1855511.1855525,
author = {Yadwadkar, Neeraja J. and Bhattacharyya, Chiranjib and Gopinath, K. and Niranjan, Thirumale and Susarla, Sai},
title = {Discovery of Application Workloads from Network File Traces},
booktitle = {Proceedings of the 8th USENIX Conference on File and Storage Technologies},
series = {FAST'10},
year = {2010},
location = {San Jose, California},
pages = {14--14},
numpages = {1},
url = {http://dl.acm.org/citation.cfm?id=1855511.1855525},
acmid = {1855525},
publisher = {USENIX Association},
address = {Berkeley, CA, USA},
}

Teaching

EE282: Computer Systems Architecture, Winter 2019
Co-Instructor with Prof. John Hennessy
CS162: Operating Systems and Systems Programming, Fall 2017
Graduate Student Instructor with Prof. Ion Stoica
Also, taught a couple of lectures (and it was a lot of fun!):
- Caching (Finished), Demand Paging [slides: pptx pdf]
- General I/O [slides: pptx pdf]
CS162: Operating Systems and Systems Programming, Spring 2013
Graduate Student Instructor with Prof. Anthony Joseph

Academic Service

Diversity and Inclusion Chair, SoCC 2021
Co-Organizer, MLArchSys: Workshop on ML for Computer Architecture and Systems, ISCA 2021
Program Committee, WORDS: Workshop On Resource Disaggregation and Serverless, ASPLOS 2021
Area Chair - SysML (Systems for ML and ML for Systems), Journal of Systems Research (JSys), 2021
Area Co-Chair - Serverless Computing, Journal of Systems Research (JSys), 2021
Co-Founder, Journal of Systems Research (JSys), 2020
Program Committee, Conference on Machine Learning and Systems (MLSys), 2021
External Reviewer, ASPLOS 2021
Program Committee, SoCC 2020
Program Committee, Workshop on Machine Learning for Systems at NeurIPS 2020
Reviewer, ICML 2020
Program Committee, Systems and Machine Learning Conference (SysML), 2020
Poster Co-chair, Poster Program Committee, SoCC 2019
Program Committee, SoCC 2019
Program Committee, HotCloud 2019
Program Committee, Workshop on Resource Disaggregation (WORD), ASPLOS 2019
Reviewer, ICML 2019
Reviewer, NeurIPS 2019
Reviewer, NeurIPS 2018
External reviewer, OSDI 2016
External reviewer, Usenix ATC 2015
External reviewer, FAST 2014

Talks

Machine Learning for Resource Management
Chalmers AI Research Centre, Chalmers University of Technology, Gothenberg, Sweden, May 2019
A Case for Managed and Model-less Inference Serving
The 17th workshop on Hot Topics in Operating Systems (HotOS'19), Bertinoro, Italy, May 15th, 2019
Model-based Resource Allocation in the Public Cloud
Platforms Lab Seminar, Stanford, CA, January 2019
Machine Learning for resource management in Distributed Systems
Invited Speaker at Workshop on ML for Systems at NeurIPS 2018, December 8th, 2018
Machine Learning for Resource Management in the Datacenter and the Cloud
Lawrence Berkeley National Lab, Berkeley, CA, January 2018
Machine Learning for Resource Management in the Datacenter and the Cloud
Platforms Lab, Stanford, CA, November 2017
Machine Learning for Resource Management in the Datacenter and the Cloud
Microsoft Research, Redmond, WA, November 2017
Selecting the Best VM across Multiple Public Clouds: A Data-Driven Performance Modeling Approach
ACM Symposium on Cloud Computing (SoCC), Santa Clara, CA, September 2017
Selecting the Best VM across Multiple Public Clouds using PARIS: A Data-Driven Performance Modeling Approach
RISELab/VMware Day, Berkeley, CA, May 2017
Selecting the Best VM across Multiple Public Clouds using PARIS: A Data-Driven Performance Modeling Approach
Google, Mountain View, CA, May 2017
Data-Driven Modeling for Cloud-Hosted Systems' Management and Optimization
Smule, San Francisco, CA, Jan 2017
Let your Workloads Choose your VMs in the Cloud using PARIS
RISELab Winter Retreat, Berkeley, CA, Jan 2017
Data-Driven Modeling for Cloud Management and Optimization
Splunk, San Francisco, CA, July 2016
Data-Driven Modeling for System Management and Optimization
SAP Dublin, CA, June 2016
PARIS: Model Based Performance Estimation Across the Cloud
AMPLab Summer Retreat June 2016
Managing Sample Bias in a Model-Based Cluster Resource Manager
AMPLab Summer Retreat June 2016
The Judgement of PARIS: Performance-Aware Resource Inference System
Microsoft Research, Redmond, Intern Talk, August 2015 and AMPLab Winter Retreat, January 2016
Faster Jobs in Distributed Processing Systems using Machine Learning
Department Seminar, Department of Computer Science and Automation (CSA), Indian Institute of Science (IISc), May 2015
Faster Jobs in Distributed Data Processing using Multi-Task Learning
SIAM International Conference on Data Mining (SDM), April 2015
Wrangler: Predictable and Faster Jobs using Fewer Resources
ACM Symposium on Cloud Computing (SoCC), November 2014
Wrangler: A Machine Learning Approach for Straggler Avoidance
AMPLab Summer Retreat, May 2014 and AMPLab All Hands 2014
Zone Localization Methods and Services
Software Defined Buildings (SDB) Winter Retreat, Jan 2014
Discovery of Application Workloads from Network File Traces
Usenix Conference on File and Storage Technologies (FAST) Feb 2010 and Riverbed Technology, Feb 2010

News

[NEW] Co-organizing ML for Computer Architecture and Systems (MLArchSys) co-located with ISCA 2021
[NEW] Serving on the Program Committee of WORDS 2021: Workshop On Resource Disaggregation and Serverless co-located with ASPLOS 2021
[NEW] SmartHarvest accepted at EuroSys 2021!
[NEW] Thrilled to be serving as the “Diversity and Inclusion Chair” for ACM SoCC 2021
Co-founded the Journal of Systems Research (JSys)
Moderating "Ask Me Anything” session with Kim Keeton as the guest at OSDI 2020
Serving on the Program Committee of MLSys 2021
Panelist for “Serverless Computing” panel at ACM SoCC 2020
Served on the Program Committee of Workshop on ML for Systems at NeurIPS 2020
Serving on the Program Committee of ACM SoCC 2020
Serving on the Program Committee of HotStorage 2020. Call for papers available now!
Serving as a reviewer for ICML 2020. Call for papers available now!
Serving on the organizing committee for “ML for Computer Architecture and Systems workshop at ISCA”
Serving on the Program Committee of the AAAI-20 Workshop on Cloud Intelligence. Call for papers available now!
Invited to talk about "Research in Machine Learning for Systems: Insights and Guidelines", at the IBM Research Student Workshop on Systems and Cloud, Nov. 19th!
Selected to participate in Rising stars in EECS 2019
INFaaS pre-print available on Arxiv
Serving as Poster Co-chair for ACM SoCC 2019. Call for posters available now!
Serving on the Program Committee of SysML 2020
Serving on the Program Committee of ACM SoCC 2019
INFaaS source code available now!